
print("Hello Abdulgafar")

Hello Abdulgafar


# Area of  rectangle
length=40
breadth=6

Area=length*breadth

print('Area of Rectangle=',Area)

Area of Rectangle= 240


#len Function

len('LONDON')

6


100==100

True


600!=600

False


p=54


type(p)

int


#int to float
h=float(p)
h

54.0


#float to int
x=12.9


type(x)

float


y=int(x)
y

12


city=input("Enter any city Name = ")

Enter any city Name = London


city

'London'


a=input("Enter the value of a ")

Enter the value of a 12


b=input("Enter the value of b ")

Enter the value of b 20


int(a)+int(b)

32


#Area of a circle
pi=3.14

radius=float(input("Enter the raduis"))
print("radius=",radius)

area=pi*(radius**2)
print("The area of cicleis=",round(area,2))

Enter the raduis22
radius= 22.0
The area of cicleis= 1519.76


name=input("Enter any name is-: ")

Enter any name is-: Abdulgafar


#List
list1=[]

list2=[1,2,3,4,5,]

list3=["pen","book","chair"]

list4=[45,65,"pen","book",True ]
list4

[45, 65, 'pen', 'book', True]


list2[-2]

4


list8=[45,67,89,54,82,90,22,43,21]
list8[0:3]

[45, 67, 89]


fruitlist=["kiwi","Guava","Grapes","pineaple","Berries","Banana","Apple","Mango"]
fruitlist

['kiwi', 'Guava', 'Grapes', 'pineaple', 'Berries', 'Banana', 'Apple', 'Mango']


fruitlist.pop()

'Mango'


fruitlist

['kiwi', 'Guava', 'Grapes', 'pineaple', 'Berries', 'Banana', 'Apple']


fruitlist[3]=("mango")


fruitlist

['kiwi', 'Guava', 'Grapes', 'mango', 'Berries', 'Banana', 'Apple']


fruitlist.insert(1,"nut")
fruitlist

['kiwi', 'nut', 'Guava', 'Grapes', 'mango', 'Berries', 'Banana', 'Apple']


fruitlist

['kiwi', 'nut', 'Guava', 'Grapes', 'mango', 'Berries', 'Banana', 'Apple']


fruitlist.append("Grapes")


fruitlist

['kiwi',
 'nut',
 'Guava',
 'Grapes',
 'mango',
 'Berries',
 'Banana',
 'Apple',
 'Grapes']


del fruitlist

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_10140\1934137106.py in <module>
----> 1 del fruitlist

NameError: name 'fruitlist' is not defined


carlist1=["skoda","Innova","Hectar"]
carlist1

['skoda', 'Innova', 'Hectar']


carlist1.append("Thar")
carlist1

['skoda', 'Innova', 'Hectar', 'Thar']


carlist1.append(["Audi","BMW","Fortuner"])
carlist1

['skoda', 'Innova', 'Hectar', 'Thar', ['Audi', 'BMW', 'Fortuner']]


carlist2=["bolero","Santro","Tigor"]
carlist2

['bolero', 'Santro', 'Tigor']


carlist2.extend(["seltos","Bentley","Tiago","Harrier","tesla"])
carlist2

['bolero', 'Santro', 'Tigor', 'seltos', 'Bentley', 'Tiago', 'Harrier', 'tesla']


carlist2.append("Jaguar")
carlist2

['bolero',
 'Santro',
 'Tigor',
 'seltos',
 'Bentley',
 'Tiago',
 'Harrier',
 'tesla',
 'Jaguar']


carlist2[3]="Benz"
carlist2

['bolero',
 'Santro',
 'Tigor',
 'Benz',
 'Bentley',
 'Tiago',
 'Harrier',
 'tesla',
 'Jaguar']


carlist2.clear()


carlist2

[]


t1=(1,24,54,9)
t1

(1, 24, 54, 9)


t1[2]

54


t1[0]

1


d1={"employee":7010,"Name":"Abdul","age":34}
d1

{'employee': 7010, 'Name': 'Abdul', 'age': 34}


d1.keys()

dict_keys(['employee', 'Name', 'age'])


d1.values()

dict_values([7010, 'Abdul', 34])


d1.items()

dict_items([('employee', 7010), ('Name', 'Abdul'), ('age', 34)])


d1["employee"]

7010


d1["age"]=40
d1

{'employee': 7010, 'Name': 'Abdul', 'age': 40}


d1.update({"salary":56789})
d1

{'employee': 7010, 'Name': 'Abdul', 'age': 40, 'salary': 56789}


d1.pop("age")

40


d1.keys()

dict_keys(['employee', 'Name', 'salary'])


d1.values()

dict_values([7010, 'Abdul', 56789])


d2={1:"mercury",2:"venus",3:"earth",4:"mars"}
d2

{1: 'mercury', 2: 'venus', 3: 'earth', 4: 'mars'}


d3={'Empno':[1,2,3],'Name':['Jack','Jhon','Smith'],'Age':[34,35,38],}

d3

{'Empno': [1, 2, 3], 'Name': ['Jack', 'Jhon', 'Smith'], 'Age': [34, 35, 38]}


d3.keys()

dict_keys(['Empno', 'Name', 'Age'])


d3.values()

dict_values([[1, 2, 3], ['Jack', 'Jhon', 'Smith'], [34, 35, 38]])


d3.keys()

dict_keys(['Empno', 'Name', 'Age'])


d3.items()

dict_items([('Empno', [1, 2, 3]), ('Name', ['Jack', 'Jhon', 'Smith']), ('Age', [34, 35, 38])])


d3['Name'][2]

'Smith'


d3['Name'][2] ='Abdul'

d3

{'Empno': [1, 2, 3], 'Name': ['Jack', 'Jhon', 'Abdul'], 'Age': [34, 35, 38]}


d3.get('Name')

['Jack', 'Jhon', 'Abdul']

d3

{'Empno': [1, 2, 3], 'Name': ['Jack', 'Jhon', 'Abdul'], 'Age': [34, 35, 38]}


s1={11,45,65,87,98,88,77}
s1

{11, 45, 65, 77, 87, 88, 98}


s2={11,11,56,76,76,99}
s2

{11, 56, 76, 99}


days={"Sunday","monday","tuesday","wednesday","thursday","friday"}
days

{'Sunday', 'friday', 'monday', 'thursday', 'tuesday', 'wednesday'}


#initization 
i=1
# condition
while (i<5):
    print(i)
    i=1+i #increment


i=1
while(i<7):
    print(i)
    i=i+1


k=1
while(k<4):
    print(k)
    k=k+1

1
2
3


j=1
while(j<=15):
    print(j,end=" ")
    j=j+1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


j=0
while(j<=40):
    print(j,end=" ")
    j=j+5

0 5 10 15 20 25 30 35 40


p=0

while(p<=5):
    print(p)
    print("loop")
    p=p+1
    
print("End Of Loop")

0
loop
1
loop
2
loop
3
loop
4
loop
5
loop
End Of Loop


p=0

while(p<=5):
    print(p,end=" ")
    print("loop")
    p=p+1
    
print("End Of Loop")

0 loop
1 loop
2 loop
3 loop
4 loop
5 loop
End Of Loop


i=1

while(i<=10):
    print("2","*",i,"=",2*i)
    i=i+1

2 * 1 = 2
2 * 2 = 4
2 * 3 = 6
2 * 4 = 8
2 * 5 = 10
2 * 6 = 12
2 * 7 = 14
2 * 8 = 16
2 * 9 = 18
2 * 10 = 20


i=1
j=input("Enter value for table generated ")
while(i<=10):
    print(j,"*",i,"=",2*i)
    i=i+1

Enter value for table generated 10
10 * 1 = 2
10 * 2 = 4
10 * 3 = 6
10 * 4 = 8
10 * 5 = 10
10 * 6 = 12
10 * 7 = 14
10 * 8 = 16
10 * 9 = 18
10 * 10 = 20


i=1
total=0
while(i<10):
    total=total+i
    print(total)
    i=i+1
print("Grand total =",total)

1
3
6
10
15
21
28
36
45
Grand total = 45


for index in [10,20,30]:
    print(index)

10
20
30


for index in [55,66,77,88,99]:
    print(index)

55
66
77
88
99


for i in [12,67,87,89]:
    print(i)

12
67
87
89


for j in [10,30,74,90,98]:
    print(j)

10
30
74
90
98


for k in (15,46,90,76):
    print(k)

15
46
90
76


dict1={1:"sun",2:"monn",3:"planets"}
dict1

{1: 'sun', 2: 'monn', 3: 'planets'}


for i in dict1:
    print(i)

1
2
3


for i in dict1:
    print(dict1[i])

sun
monn
planets


for i in dict1:
    print(i,"=>",dict1[i])

1 => sun
2 => monn
3 => planets


for j in [10,30,74,90,98]:
    print(j)
    print("loop")
print("End Of Loop")

10
loop
30
loop
74
loop
90
loop
98
loop
End Of Loop


range(3)

range(0, 3)


list(range(5))

[0, 1, 2, 3, 4]


list(range(15))

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]


list(range(1,10))

[1, 2, 3, 4, 5, 6, 7, 8, 9]


list(range(2,20))

[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]


list(range(2,22,2))

[2, 4, 6, 8, 10, 12, 14, 16, 18, 20]


list(range(5,55,5))

[5, 10, 15, 20, 25, 30, 35, 40, 45, 50]


for i in range(5):
    print(i)


for k in range(7):
    print(k,end=" ")

0 1 2 3 4 5 6


for p in range(0,10,2):
    print(p)


for p in range(0,20,2):
    print(p,end=" ")

0 2 4 6 8 10 12 14 16 18


number=int(input("Enter number to be used for generating table "))
for i in range(1,11):
    print(number,"*",i,"=",number*i)

Enter number to be used for generating table 10
10 * 1 = 10
10 * 2 = 20
10 * 3 = 30
10 * 4 = 40
10 * 5 = 50
10 * 6 = 60
10 * 7 = 70
10 * 8 = 80
10 * 9 = 90
10 * 10 = 100


citylist=[]

for p in range(3):
    city=input("Enter any city name")
    citylist.append(city)
    print(citylist)
    
print("Complete city list= ",citylist)

Enter any city namekano
['kano']
Enter any city namelagos
['kano', 'lagos']
Enter any city nameibadan
['kano', 'lagos', 'ibadan']
Complete city list=  ['kano', 'lagos', 'ibadan']


marks=90

if marks>50:
    print("Good")
else:
    print("Need to work hard")

Good


marks=90
if marks>50:
    print("Good")
else:
    pass

Good


marks=40

if marks>50:
    print("Good")
else:
    print("Need to work hard")

Need to work hard


num=15

if num%2==0:
    print("Even number")
else:
    print("odd number")

odd number


num=int(input("enter any  number = "))

if num%2==0:
    print("Even number")
else:
    print("odd number")

enter any  number = 10
Even number


marks=int(input("Enter any marks"))
if marks>90:
    print("Excellent")
elif marks>80 and marks<90:
    print("Very Good")
elif marks>70 and marks<80:
    print("Good")
elif marks>60 and marks<70:
    print("Average")
else:
    print("Work hard")


#Break  and continue statement:

for i in [10,20,30,40]:
    print(i)


for i in [10,20,30,40]:
    if i==30:
        break
    print(i)


for i in ['sun','moon','earth','water']:
    if i=='moon':
        break
    print(i)


for i in [10,20,30,40]:
    if i==30:
        continue
    print(i)


for i in ['sun','moon','earth','venus','water']:
    if i=='venus':
        continue
    print(i)


#List comprehension

m=[i for i in [10,20,30,40,50,60]]
m


p=[i for i in range(1,20) if i%2==0]
p


k=[i for i in range(1,20) if i%2!=0]
k


world1="COMPUTER"
world1.lower()


world2="mobile"
world2.upper()


world2.upper()
w1=world2.upper()
w1


w1.isupper()


w1.islower()


world2.islower()


world3='planeTS'
world3.swapcase()


a='Twitter is a renamed by x'
a


a.split()


b="elon musk is a ceo of x"
b


b.capitalize()


b.title()


c="MarsRover lands on Mars Planet"
c


c.startswith('MarsRover')


c.endswith('Planet')


p="ABCDEF"


p.isalpha()


m="567786544"
m.isdigit()


m.isalpha()


k="698ASDF"
k


k.isalnum()


world="Elon musk launched AI Car Tesla"
world


len(world)


world.find('Car')


world


world.find('AI',16,30)


world.count('a')


world.count('s')


world1="NASA is building hotel project in Mars"
world1


world1.replace("NASA","SPACEX")


text=["NASA is building hotel project in Mars"]
text


''.join(text)


' '.join(text)


mystring=' '
mystring.join(text)


# casefold 
string1="Tesla"
string2="tesla"

if string1.casefold()==string2.casefold():
    print("The two strings are matching")
else:
    print("The two string are not matching")


string1="Tesla"
string2="tesla"

if string1==string2:
    print("The two strings are matching")
else:
    print("The two string are not matching")


help(max)


help()


def function1():
    print("AM in function1")


function1()


def myfunction():
    print("inside myfunction")


myfunction()


def addfunction():
    a=10
    b=20
    c=a+b
    print("The sum is =",c)


addfunction()


def divisionfunction():
    a=80
    b=5
    c=a/b
    print("The division is",c)


divisionfunction()


def substract():
    a=10
    b=5
    c=a-b
    print("The substract=",c)


substract()


def square_function():
    s=8
    sq=s**2
    print("The Square of s is",sq)
square_function()


def rectarea_func():
    length=int(input("Enter length"))
    breadth=int(input("Enter breadth"))
    area=length*breadth
    print("The area of rectanngle =",area)
rectarea_func()


def area_circle_function():
    pi=3.14
    radius=77
    area_circle=pi*(radius**2)
    print("The area of circle is= ",area_circle)
area_circle_function()


def area_circle_function():
    pi=3.14
    radius=float(input("Enter the value of radius"))
    area_circle=pi*(radius**2)
    print("The area of circle is= ",area_circle)
area_circle_function()


def add_func(a,b):
    c=a+b
    print("sum",c)


a=5
b=10
add_func(a,b)


def multiply_func(a,b):
    c=a*b
    print("Multiply=",c)
    
a=80
b=2
multiply_func(a,b)


def are_rectf(l,b):
    area=l*b
    print("Area of rectangle=",area)
    
l=49
b=9

are_rectf(l,b)


def substract_fun(l,b):
    c=a-b
    print("substract =",c)
    
l=int(input("Enter any length"))
b=int(input("Enter any breadth"))

substract_fun(l,b)


#variable number of argument--*args
def tesfunc(*args):
    print(sum(args))
    
    
tesfunc(1,2)


tesfunc(1,2,3)


def testminfunc(*args):
    print(min(args))
    
testminfunc(2,4,5,6,3)


def testminfunc(*args):
    print(min(args))
    
testminfunc(90,80,45)


def testmaxfunc(*args):
    print(max(args))
    
testmaxfunc(2,4,5,6,3)


# global variable

age=21
age


def testfuntion():
    marks=80   #  local variable
    print(marks)
    print("age = ",age)
testfuntion()


def addfunction():
    a=10
    b=5
    result=a+b
    return result
myvar=addfunction()
myvar**2


def multiplyfunc():
    x=8
    y=3
    z=x*y
    print(z)
    
multiplyfunc()


def f1():
    for i in range(1,10):
        if i==4:
            return
        print(i)


f1()


def f2(*args):
    return(sum(args))
    
f2(1,2,3,4,5)


f2(2,4,9,10,24)


def my_sum(*args):
    result = 0 
    #iterating over the python args
    for x in args:
        result += x
    return result
print(my_sum(1,2,3,4))


def add(*numbers):
    total= 0
    for num in numbers:
        total += num
    return total


print(add(2,3))
print(add(2,3,4))
print(add(2,3,4,5))
print(add(2,3,4,5,6))


#  **kwarg----> keywords argument

def concatenate(**kwargs):
    result= ""
    #iterating over the python kwargs dictionary
    for arg in kwargs.values():
        result += arg
    return result
print(concatenate(a="Real ",b="python ",c="great ",e="enjoy"))


def concatenate(**kwargs):
    result= ""
    #iterating over the python kwargs dictionary
    for arg in kwargs: # keys
        result += arg
    return result
print(concatenate(a="Real ",b="python ",c="great ",e="enjoy"))


def addfunc():
    a=int(input("Enter a"))
    b=int(input("Enter b"))
    c=a+b
    return c

def minusfunc():
    x=80
    y=7
    z=x-y
    return z


def calculator_func():
    addresult=addfunc()
    print("Addition",addresult)
    minusresult=minusfunc()
    print("minus",minusresult)
calculator_func()


# Lambda Funtions
#lambda variable: expression
t= lambda x:x+2

t(5)


t(80)


j=lambda p:p+10

j(50)


j(160)


h=lambda a:a**2

h(8)


h(12)


h(9)


t=lambda a,b:a*b


t(10,30)


t(90,10)


list1=[10,20,30,40]
list(map(lambda x:x+2,list1))

[12, 22, 32, 42]


list1=[10,20,30,40,50,60,70,80]

list(filter(lambda x:x>40,list1))

[50, 60, 70, 80]


def myf():
    print('hello ')


i=0
while(i<5):
    print(i)
    i=i+1
    print('hello')

0
hello
1
hello
2
hello
3
hello
4
hello


def testf(k):
    result=30/k
    print(result)


testf(2)

15.0

Enter value0
Handling run time error division by zero


k=int(input("Enter value"))
testf(k)

Enter value0

---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_20364\3180007702.py in <module>
      1 k=int(input("Enter value"))
----> 2 testf(k)

~\AppData\Local\Temp\ipykernel_20364\292434318.py in testf(k)
      1 def testf(k):
----> 2     result=30/k
      3     print(result)

ZeroDivisionError: division by zero


# how handle the error
try:
    k=int(input("Enter value"))
    testf(k) 
except ZeroDivisionError as err:
    print('Handling run time error',err)

Enter value0
Handling run time error division by zero


try:
    i=int(input('value'))
    p=50/i
except ZeroDivisionError:
    print('Zero division error')
except NameError:
    print('check name of variable :Name error')
else:
    print(p)

value8
6.25


a=[10,20,30]
try:
    print(a[0])
    print(a[1])
    print(a[2])
except:
    print("out of range")
else:
    print("hello")
finally:
    print("end of the program")

10
20
30
hello
end of the program


class student:
    #initialization constructor
    def __init__(self,rollno,name):
        self.rollno=rollno
        self.name=name
    def display(self):
        print("Rollno=",self.rollno)
        print("Name=",self.name)


# instance/object
stud1=student(1,'abdul')
stud1.display()

Rollno= 1
Name= abdul


stud2=student(2,'zainab')
stud2.display()

Rollno= 2
Name= zainab


stud1.name

'abdul'


stud3=student(3,'smit')
stud3.display()

Rollno= 3
Name= smit


class Employee:
    company='Google' # class variable
    def __init__(self,empno,name):
        self.empno=empno
        self.name=name
        
    def display(self):
        print('Empno',self.empno)
        print('Name',self.name)


emp1=Employee(101,'Toyin')
emp1.display()

Empno 101
Name Toyin


Employee.company

'Google'


class car:
    
    # simple class or cars
    
    #construct to initialize
    def __init__(self,company,color):
        self.company=company
        self.color=color
        
    #function to print car company and color
    def display(self):
        print('This is a',self.color,self.company)
        
ca=car("Ferrari","Red")
ca.display()

This is a Red Ferrari


class Car:
    def __init__(self, model, color):
        self.model = model
        self.color = color

    def display(self):
        print(f"Model: {self.model}, Color: {self.color}")

def main():
    c = Car("Ferrari", "Red")
    c.display()

if __name__ == "__main__":
    main()

Model: Ferrari, Color: Red


class player:
    def __init__(self,name,age,sport):
        print('Iniatialized data for players')
        self.name=name
        self.age=age
        self.sport=sport
        
    def getdata(self,name,age,sport):
        print("Getting data for players")
        self.name=name
        self.age=age
        self.sport=sport
        
    def displaydata(self):
        print("Player details :",self.name,self.age,self.sport)
        
p1=player("Virat",35,"Cricket")
p1.displaydata()    

p1.getdata("Richard",25,"Tennis")
p1.displaydata()

Iniatialized data for players
Player details : Virat 35 Cricket
Getting data for players
Player details : Richard 25 Tennis


import keyword


keyword.kwlist

['False',
 'None',
 'True',
 '__peg_parser__',
 'and',
 'as',
 'assert',
 'async',
 'await',
 'break',
 'class',
 'continue',
 'def',
 'del',
 'elif',
 'else',
 'except',
 'finally',
 'for',
 'from',
 'global',
 'if',
 'import',
 'in',
 'is',
 'lambda',
 'nonlocal',
 'not',
 'or',
 'pass',
 'raise',
 'return',
 'try',
 'while',
 'with',
 'yield']


import sys


sys.path

['C:\\Users\\USER',
 'C:\\Users\\USER\\anaconda3\\python39.zip',
 'C:\\Users\\USER\\anaconda3\\DLLs',
 'C:\\Users\\USER\\anaconda3\\lib',
 'C:\\Users\\USER\\anaconda3',
 '',
 'C:\\Users\\USER\\anaconda3\\lib\\site-packages',
 'C:\\Users\\USER\\anaconda3\\lib\\site-packages\\win32',
 'C:\\Users\\USER\\anaconda3\\lib\\site-packages\\win32\\lib',
 'C:\\Users\\USER\\anaconda3\\lib\\site-packages\\Pythonwin',
 'C:\\Users\\USER\\anaconda3\\lib\\site-packages\\IPython\\extensions',
 'C:\\Users\\USER\\.ipython']


import os


os.listdir()

['.ipynb_checkpoints',
 '.ipython',
 '.jupyter',
 'anaconda3',
 'AppData',
 'Application Data',
 'Contacts',
 'Cookies',
 'Desktop',
 'Documents',
 'Downloads',
 'Favorites',
 'Links',
 'Local Settings',
 'Microsoft',
 'Music',
 'My Documents',
 'NetHood',
 'NTUSER.DAT',
 'ntuser.dat.LOG1',
 'ntuser.dat.LOG2',
 'NTUSER.DAT{f28145ce-bc8e-11ee-8044-a7aa5cff8d87}.TM.blf',
 'NTUSER.DAT{f28145ce-bc8e-11ee-8044-a7aa5cff8d87}.TMContainer00000000000000000001.regtrans-ms',
 'NTUSER.DAT{f28145ce-bc8e-11ee-8044-a7aa5cff8d87}.TMContainer00000000000000000002.regtrans-ms',
 'ntuser.ini',
 'OneDrive',
 'OneDrive - Pearl The SE',
 'Pictures',
 'PrintHood',
 'PycharmProjects',
 'Recent',
 'Saved Games',
 'Searches',
 'SendTo',
 'Start Menu',
 'Templates',
 'Untitled.ipynb',
 'Videos',
 'Welcome To Python.ipynb']


import math


math.pi

3.141592653589793


math.sqrt(64)

8.0


math.sqrt(91)

9.539392014169456


math.pow(2,2)

4.0


math.pow(9,2)

81.0


math.__dict__.keys()

dict_keys(['__name__', '__doc__', '__package__', '__loader__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'dist', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'isclose', 'isfinite', 'isinf', 'isnan', 'isqrt', 'lcm', 'ldexp', 'lgamma', 'log', 'log1p', 'log10', 'log2', 'modf', 'pow', 'radians', 'remainder', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc', 'prod', 'perm', 'comb', 'nextafter', 'ulp', 'pi', 'e', 'tau', 'inf', 'nan'])


import math

radius=98
area=math.pi*(radius**2)
print(area)

30171.855845076374


import math as mt

mt.sqrt(81)

9.0


radius=98
area=mt.pi*(radius**2)
print(area)

30171.855845076374


from math import pow
pow(4,2)

16.0


#get current date
import datetime
datetime.datetime.now()

datetime.datetime(2024, 3, 17, 3, 41, 21, 119324)


d1=datetime.date.today()
d1

datetime.date(2024, 3, 17)


datetime.date(2024,2,7)

datetime.date(2024, 2, 7)


d1.year

2024


from datetime import date


td=date.today()
td

datetime.date(2024, 3, 17)


print("Year=",td.year)
print("Year=",td.month)
print("Days=",td.day)

Year= 2024
Year= 3
Days= 17


date.today()

datetime.date(2024, 3, 10)


from datetime import time


t1=time(6,45,23)


t1.hour

6


t1.minute

45


t1.second

23


from datetime import time
a=time()
print(a)


# time(hour,minute,second)
b=time(12,43,56)
print(b)

00:00:00
12:43:56


print("hour",b.hour)
print("minute",b.minute)
print("second",b.second)

hour 12
minute 43
second 56


import time
time.localtime()

time.struct_time(tm_year=2024, tm_mon=3, tm_mday=17, tm_hour=6, tm_min=18, tm_sec=32, tm_wday=6, tm_yday=77, tm_isdst=0)


from datetime import datetime
a=datetime(2022,8,14)
print(a)

2022-08-14 00:00:00


print("year",a.year)
print("month",a.month)
print("day",a.day)

year 2022
month 8
day 14


b=datetime(2022,8,14,12,47,50)
print(b.year)
print(b.month)
print(b.day)
print(b.hour)
print(b.minute)
print(b.second)

2022
8
14
12
47
50


dd=open('c:\\now\\demomarch','w')


dd.write("we are enjoying python commands")
dd.close()


rfile=open('c:\\now\\demomarch','r')
print(rfile.read())

rfile.close()

we are enjoying python commands


appfile=open('c:\\now\\demomarch','a')
appfile.write("\n Republic day is coming this week")
appfile.close()


rfile=open('c:\\now\\demomarch','r')
print(rfile.read())

rfile.close()

we are enjoying python commands
 Republic day is coming this week


#code re-use inheritance
class Bird:
    def speak(self):
        print("Bird Speaking")


#child class sparrow inherits the base class Bird
class Sparrow(Bird):
    def chirp(self):
        print("Sparrow chirping")
        
#instance/object creation
sp= Sparrow()
sp.chirp()
sp.speak()

Sparrow chirping
Bird Speaking


class Grandfather:
    def speak(self):
        print("Grandfather speaking")
        
        
class father(Grandfather):
    def sleep(self):
        print("father sleeping")
        
class child(father):
    def eat(self):
        print("Eating bread")
        
        
ch= child()
ch.speak()
ch.sleep()
ch.eat()

Grandfather speaking
father sleeping
Eating bread


# Multiple inheritance
class calculation1:
    def summation(self,a,b):
        return a+b;
    
class calculation2:
    def multiplication(self,a,b):
        return a*b;
    
class Derived(calculation1,calculation2):
    def Divide(self,a,b):
        return a/b;
    
d=Derived()

print(d.summation(10,20))
print(d.multiplication(10,20))
print(d.Divide(10,20))

30
200
0.5


# method overriding
class Animal:
    def speak(self):
        print("speaking")
        
class dog(Animal):
    def speak(self):
        print("Barking")
        
        
d=dog()
d.speak()

Barking


class Bank:
    def getroi(self):
        return 11;
    
class SBI(Bank):
    def getroi(self):
        return 7;
    
class ICICI(Bank):
    def getroi(self):
        return 8;
    
    
b1= Bank()
b2= SBI()
b3= ICICI()

print("Bank of Interest",b1.getroi());
print("SBI Rate of Interest",b2.getroi());
print("ICICI Rate of Interest",b3.getroi())

Bank of Interest 11
SBI Rate of Interest 7
ICICI Rate of Interest 8


#Data abstraction
class Employee:
    __count = 0;
    
    def __init__(self):
        Employee.__count = Employee.__count+1
        
    def display(self):
        print("The number of employee",Employee.__count)


        emp = Employee()


try:
    print(emp.__count)
except(AttributeError):
    print('AttributeError:Employee object has no attribute __count')
    
finally:
    emp.display()

AttributeError:Employee object has no attribute __count
The number of employee 1


import re


pattern = "Data science"
text = "Data science is a stream and it is of AI. You can solve many complex problems using Data science tools and techs"
match = re.findall(pattern,text)
print(match)

['Data science', 'Data science']


pattern = "\d+"
text = "There are 123 apples and 456 oranges"
matches= re.findall(pattern,text)
print(matches)

['123', '456']


price = ["apple cost £60","mango cost £100"]

for msg in price:
    match = re.findall("\d+",msg)
    print(match)

['60']
['100']


test = "India China USA"
x = re.search("France",text)
print(x)

None


msg1 = "this product is really great"
search = re.search("^this.*great$",msg1)
print(search)

<re.Match object; span=(0, 28), match='this product is really great'>


import numpy as np


np.array([1,2,3,4,5,6])

array([1, 2, 3, 4, 5, 6])


a=np.array([11,22,13,14,15])
a

array([11, 22, 13, 14, 15])


b=np.array([12.4,60.7,8.8])
b

array([12.4, 60.7,  8.8])


c=np.array([44,65,90,87])
c

array([44, 65, 90, 87])


d=np.array([22,77,98,21,24,99,54])
d

array([22, 77, 98, 21, 24, 99, 54])


e=np.array([[1,2],[3,4]])
e

array([[1, 2],
       [3, 4]])


e.shape

(2, 2)


f=np.array([[11,12],[13,14]])
f

array([[11, 12],
       [13, 14]])


f.shape

(2, 2)


g=np.array([[10,20],[30,40],[50,60]])
g

array([[10, 20],
       [30, 40],
       [50, 60]])


g.shape

(3, 2)


h=np.array([[10,20,30],[40,50,60],[70,80,90]])
h

array([[10, 20, 30],
       [40, 50, 60],
       [70, 80, 90]])


h.shape

(3, 3)


h.ndim

2


h.max()

90


# total number of element
h.size

9


# bytesize in memory
h.itemsize

4


a.size

5

a

array([11, 22, 13, 14, 15])


a.shape

(5,)


i=np.array([[1,2,3],[4,5,6]])
i

array([[1, 2, 3],
       [4, 5, 6]])


i.shape

(2, 3)


i.reshape(3,2)

array([[1, 2],
       [3, 4],
       [5, 6]])


p=np.arange(9)
p

array([0, 1, 2, 3, 4, 5, 6, 7, 8])


p.reshape(3,3)

array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])


k=np.arange(35)
k

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34])


k.reshape(7,5)

array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24],
       [25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34]])


e.dtype

dtype('int32')


x=np.array([[34,56,],[24,56]],dtype='float64')
x

array([[34., 56.],
       [24., 56.]])


x.dtype

dtype('float64')


x.itemsize

8


type(x)

numpy.ndarray

x

array([[34., 56.],
       [24., 56.]])


x.ravel()

array([34., 56., 24., 56.])


np.zeros([3,4])

array([[0., 0., 0., 0.],
       [0., 0., 0., 0.],
       [0., 0., 0., 0.]])


np.ones([3,4])

array([[1., 1., 1., 1.],
       [1., 1., 1., 1.],
       [1., 1., 1., 1.]])


np.arange(5,50)

array([ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
       22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
       39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])


np.arange(5,50,5)

array([ 5, 10, 15, 20, 25, 30, 35, 40, 45])


a=np.array([2,3,4])
a

array([2, 3, 4])


b=np.array([1,2,3])
b

array([1, 2, 3])

a+b

array([3, 5, 7])

a*b

array([ 2,  6, 12])


s=np.array([5,6,7])
s

array([5, 6, 7])


p=np.array([2])
p

array([2])

s*p

array([10, 12, 14])


# Broadcasting Numpy
a=np.array([20,30,40,50])
b=np.arange(4)
print(a)
print(b)

[20 30 40 50]
[0 1 2 3]


c=a-b
print(c)

[20 29 38 47]


b**2

array([0, 1, 4, 9], dtype=int32)


b=np.random.random([2,3])
b

array([[0.35871876, 0.40111299, 0.8023355 ],
       [0.94337907, 0.07529619, 0.43047038]])


c=np.ones([2,3])
c

array([[1., 1., 1.],
       [1., 1., 1.]])


d=b+c
d

array([[1.35871876, 1.40111299, 1.8023355 ],
       [1.94337907, 1.07529619, 1.43047038]])


o=np.arange(4)
o

array([0, 1, 2, 3])


np.exp(o)

array([ 1.        ,  2.71828183,  7.3890561 , 20.08553692])


np.sqrt(o)

array([0.        , 1.        , 1.41421356, 1.73205081])


q=np.array([2,1,4,5])
q

array([2, 1, 4, 5])


np.add(q,o)

array([2, 2, 6, 8])


#index
q[2]

4


q[2:6]

array([4, 5])


a1=np.array([24,np.nan,25,90,np.nan])
a1

array([24., nan, 25., 90., nan])


np.random.rand(2,2)

array([[0.81646149, 0.70619368],
       [0.15002024, 0.22030583]])


np.random.randn(2,2)

array([[ 0.1227025 , -2.01843524],
       [-0.27544933, -1.35421677]])


# SERIES
import pandas as pd

pd.Series([1,2,3,4,5])

0    1
1    2
2    3
3    4
4    5
dtype: int64


a=pd.Series(data=[11,22,44,56,34,56,68],index=[1,2,3,4,5,6,7])
a

1    11
2    22
3    44
4    56
5    34
6    56
7    68
dtype: int64


b=pd.Series(data=[45,56,78,65,90],index=[10,11,12,13,14])
b

10    45
11    56
12    78
13    65
14    90
dtype: int64


import numpy as np
s=np.array([70,80,90,100])
s

array([ 70,  80,  90, 100])


pd.Series(s)

0     70
1     80
2     90
3    100
dtype: int32


dict1={"id":102,"Name":"Smith"}
dict1

{'id': 102, 'Name': 'Smith'}


k=pd.Series(dict1)
k

id        102
Name    Smith
dtype: object


# DATAFRAME
pd.DataFrame([1,2,3])


pd.DataFrame(data=[10,20,30,40])


e=[12,34,90,60]
L=pd.DataFrame(e)
L


data=[['Alex',10],['Bob',12],['clarke',13]]
data

[['Alex', 10], ['Bob', 12], ['clarke', 13]]


pd.DataFrame(data)


pd.DataFrame(data,columns=['Name','Age'])


pd.DataFrame(data,columns=['Name','Age'],index=[1,2,3])


pd.DataFrame(data,columns=['Name','Age'])


pd.DataFrame(data,columns=['Name','Age'],dtype=int)

C:\Users\USER\AppData\Local\Temp\ipykernel_10140\2071186554.py:1: FutureWarning: Could not cast to int32, falling back to object. This behavior is deprecated. In a future version, when a dtype is passed to 'DataFrame', either all columns will be cast to that dtype, or a TypeError will be raised.
  pd.DataFrame(data,columns=['Name','Age'],dtype=int)


dict2={'id':[1,2,3],'Name':['Abdullah','Muhammad','Faisal']}


pd.DataFrame(dict2)


pd.DataFrame(dict2,columns=['id','Name'],index=[1,2,3])


data = [{'a':1,'b':2},{'a':5,'b':10,'c':20}]
data

[{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]


pd.DataFrame(data)


pd.DataFrame(data,columns=['a','b'])


pd.DataFrame(data,columns=['a','b1'])


# BAR PLOT
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d'])
print(df)

          a         b         c         d
0  0.084494  0.593562  0.701404  0.145777
1  0.179170  0.458246  0.601145  0.703916
2  0.985736  0.657061  0.547486  0.846934
3  0.456382  0.998602  0.335720  0.308651
4  0.487489  0.755290  0.409262  0.088529
5  0.006262  0.163425  0.221446  0.543407
6  0.209881  0.130309  0.007651  0.757832
7  0.748097  0.309950  0.931806  0.988432
8  0.836019  0.184422  0.325192  0.995035
9  0.329597  0.424228  0.655226  0.856977


df.plot.bar()

<AxesSubplot:>


df.plot.barh()

<AxesSubplot:>


df.plot.hist()

<AxesSubplot:ylabel='Frequency'>


df.plot.box()

<AxesSubplot:>


df.plot.scatter(x='a',y='b')

<AxesSubplot:xlabel='a', ylabel='b'>


df = pd.DataFrame({"Day":[1,2,3,4], "Visitors":[200,100,230,300],"Bounce_Rate":[20,45,60,10]})
print(df)

   Day  Visitors  Bounce_Rate
0    1       200           20
1    2       100           45
2    3       230           60
3    4       300           10


df.set_index('Day',inplace=True)

df


df1 = df.rename(columns={"visitors":"Users"})
df1


data= {'Day':[1,2,3,4,5,6],"Visitors":[1000,700,6000,1000,400,350],"Bounce_Rate":[20,25,25,15,10,34]}
df = pd.DataFrame(data)
print(df)

   Day  Visitors  Bounce_Rate
0    1      1000           20
1    2       700           25
2    3      6000           25
3    4      1000           15
4    5       400           10
5    6       350           34


df.set_index('Day',inplace=True)

df


df.head()


df.tail()


df.sample(3)


df.head(2)


df2 = pd.read_csv('march17.csv',names=['studentid','studentname','studentage'],header=0)
df2


df2 = pd.read_csv('march17.csv',names=['studentid','studentname','studentage'],header=0,index_col='studentid')
df2


df2 = pd.read_csv('march17.csv',sep=',')
df2


df2.to_csv('march17copy.csv')


df3=pd.read_csv('march17copy.csv')
df3


#write in tsv format
# pd.read_csv('agedata1.csv',sep='\t')


df4=pd.read_excel('targets.xlsx')
df4


df4.to_excel('targets.xlsx')


url=''
df5=pd.read_html(url)
df5


pip install requests

Requirement already satisfied: requests in c:\users\user\anaconda3\lib\site-packages (2.28.1)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\users\user\anaconda3\lib\site-packages (from requests) (1.26.11)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\user\anaconda3\lib\site-packages (from requests) (2022.9.14)
Requirement already satisfied: idna<4,>=2.5 in c:\users\user\anaconda3\lib\site-packages (from requests) (3.3)
Requirement already satisfied: charset-normalizer<3,>=2 in c:\users\user\anaconda3\lib\site-packages (from requests) (2.0.4)
Note: you may need to restart the kernel to use updated packages.


import requests


response=requests.get('https://en.wikipedia.org/wiki/Data_science')


response.status_code

200


df=pd.read_csv('Head_Brain_csv')
df


import pandas as pd


df=pd.read_csv('eployee.csv1')
df


df=pd.DataFrame(data=df)
df


df.head()


df.tail()


df.sample()


df.head(10)


df.tail(5)


df.sample(2)


df.dtypes

SNo          int64
Name        object
Age        float64
City        object
Country     object
Salary     float64
dtype: object


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 22 entries, 0 to 21
Data columns (total 6 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   SNo      22 non-null     int64  
 1   Name     22 non-null     object 
 2   Age      19 non-null     float64
 3   City     19 non-null     object 
 4   Country  22 non-null     object 
 5   Salary   19 non-null     float64
dtypes: float64(2), int64(1), object(3)
memory usage: 1.2+ KB


df['Salary'].min()

3900.0


df['Salary'].max()

67666.0


df['Salary'].plot.bar()

<AxesSubplot:>


df['Age'].plot.bar()

<AxesSubplot:>

df


df.loc[:,'City']

0       Toronto
1       Edmonto
2       Toronto
3      HongKong
4           NaN
5      HongKong
6       BayArea
7     Hyderabad
8       newyork
9       newyork
10     HongKong
11          NaN
12     HongKong
13          NaN
14    Hyderabad
15      newyork
16      newyork
17      Edmonto
18        Congo
19       Angola
20      Finland
21       London
Name: City, dtype: object


df.loc[:,'Name']

0        Tom
1        Lee
2       Dave
3       Anik
4      kamal
5       Geet
6     Steven
7        Ram
8       hari
9       yami
10      Anik
11     kamal
12      Geet
13    Steven
14       Ram
15      hari
16      yami
17       Lee
18    Andrew
19     soham
20      Grig
21     Kiara
Name: Name, dtype: object


df.loc[:,['Salary','City']]


df.iloc[:,0]

0      1
1      2
2      3
3      4
4      5
5      6
6      7
7      8
8      9
9     11
10    12
11    13
12    14
13    15
14    16
15    17
16    18
17    19
18    20
19    21
20    22
21    23
Name: SNo, dtype: int64


df.iloc[:,1]

0        Tom
1        Lee
2       Dave
3       Anik
4      kamal
5       Geet
6     Steven
7        Ram
8       hari
9       yami
10      Anik
11     kamal
12      Geet
13    Steven
14       Ram
15      hari
16      yami
17       Lee
18    Andrew
19     soham
20      Grig
21     Kiara
Name: Name, dtype: object


df.iloc[:,2]

0     28.0
1     31.0
2     34.0
3     26.0
4     30.0
5      NaN
6     43.0
7     38.0
8     50.0
9      NaN
10    26.0
11    30.0
12    22.0
13    43.0
14    38.0
15     NaN
16    27.0
17    31.0
18    34.0
19    32.0
20    26.0
21    27.0
Name: Age, dtype: float64


df.iloc[:,4]

0      Canada
1      Canada
2      Canada
3        Asia
4     America
5        Asia
6     America
7        Asia
8     America
9     America
10       Asia
11    America
12       Asia
13    America
14       Asia
15    America
16    America
17     Canada
18     Africa
19     Africa
20     Europe
21     Europe
Name: Country, dtype: object


df.iloc[:,5]

0     20000.0
1      3900.0
2      8000.0
3     66672.0
4         NaN
5     30007.0
6      8300.0
7     54666.0
8     67666.0
9      8888.0
10    66672.0
11    34344.0
12    30007.0
13     8300.0
14    54666.0
15    67666.0
16     8888.0
17        NaN
18    34432.0
19    34534.0
20    33451.0
21        NaN
Name: Salary, dtype: float64

df


df.iloc[0:4,0:4]

df


df.iloc[:,1]

0        Tom
1        Lee
2       Dave
3       Anik
4      kamal
5       Geet
6     Steven
7        Ram
8       hari
9       yami
10      Anik
11     kamal
12      Geet
13    Steven
14       Ram
15      hari
16      yami
17       Lee
18    Andrew
19     soham
20      Grig
21     Kiara
Name: Name, dtype: object


df.iloc[:,4]

0      Canada
1      Canada
2      Canada
3        Asia
4     America
5        Asia
6     America
7        Asia
8     America
9     America
10       Asia
11    America
12       Asia
13    America
14       Asia
15    America
16    America
17     Canada
18     Africa
19     Africa
20     Europe
21     Europe
Name: Country, dtype: object


df.iloc[0:4,0:4]


df.iloc[2:10,3:5]


df.iloc[4:11,2:4]


df.iloc[0:6,3:5]


df.iloc[1:5,0:5]


df.iloc[7:12,3:]


df.iloc[0:10,4:]


df.iloc[:,-1]

0     20000.0
1      3900.0
2      8000.0
3     66672.0
4         NaN
5     30007.0
6      8300.0
7     54666.0
8     67666.0
9      8888.0
10    66672.0
11    34344.0
12    30007.0
13     8300.0
14    54666.0
15    67666.0
16     8888.0
17        NaN
18    34432.0
19    34534.0
20    33451.0
21        NaN
Name: Salary, dtype: float64

df


df.iloc[:,-3]

0       Toronto
1       Edmonto
2       Toronto
3      HongKong
4           NaN
5      HongKong
6       BayArea
7     Hyderabad
8       newyork
9       newyork
10     HongKong
11          NaN
12     HongKong
13          NaN
14    Hyderabad
15      newyork
16      newyork
17      Edmonto
18        Congo
19       Angola
20      Finland
21       London
Name: City, dtype: object

df


df.count()

SNo        22
Name       22
Age        19
City       19
Country    22
Salary     19
dtype: int64


df['Name'].count()

22


df['Salary'].count()

19


df['Name'].unique()

array(['Tom', 'Lee', 'Dave', 'Anik', 'kamal', 'Geet', 'Steven', 'Ram',
       'hari', 'yami', 'Andrew', 'soham', 'Grig', 'Kiara'], dtype=object)


df['Name'].nunique()

14


df['City'].count()

19


df['City'].unique()

array(['Toronto', 'Edmonto', 'HongKong', nan, 'BayArea', 'Hyderabad',
       'newyork', 'Congo', 'Angola', 'Finland', 'London'], dtype=object)


df['City'].nunique()

10


print(df['Name'].describe())

count      22
unique     14
top       Lee
freq        2
Name: Name, dtype: object


df.describe()


df.describe(include='all')

df


df1=df.drop([18,19,20],axis=0)
df1


df.drop([1,2,3],axis=0,inplace=True)

df


df.drop(['Name'],axis=1,inplace=True)

df


import pandas as pd
df=pd.read_csv('hiredata')
df


df['HireDate']=pd.to_datetime(df['HireDate'])
df


#dd/mm/yyyy
daterange=pd.date_range(start='11/01/2024',end='11/20/2024',freq='D')
daterange

DatetimeIndex(['2024-11-01', '2024-11-02', '2024-11-03', '2024-11-04',
               '2024-11-05', '2024-11-06', '2024-11-07', '2024-11-08',
               '2024-11-09', '2024-11-10', '2024-11-11', '2024-11-12',
               '2024-11-13', '2024-11-14', '2024-11-15', '2024-11-16',
               '2024-11-17', '2024-11-18', '2024-11-19', '2024-11-20'],
              dtype='datetime64[ns]', freq='D')


daterange=pd.date_range(start='01/01/2024',end='12/20/2024',freq='M')
daterange

DatetimeIndex(['2024-01-31', '2024-02-29', '2024-03-31', '2024-04-30',
               '2024-05-31', '2024-06-30', '2024-07-31', '2024-08-31',
               '2024-09-30', '2024-10-31', '2024-11-30'],
              dtype='datetime64[ns]', freq='M')


import datetime
dt=datetime.datetime.now()
print(dt)

2024-03-29 06:52:31.750332


print(dt+pd.to_timedelta(10,unit='D'))

2024-04-08 06:52:31.750332


df['Day']=df['HireDate'].dt.day
df['Month']=df['HireDate'].dt.month
df['Year']=df['HireDate'].dt.year
print(df)

             Name   HireDate   Salary  SickDaysremaining  Day  Month  Year
0  Graham Chapman 2014-03-15  50000.0                 10   15      3  2014
1     John Cleese 2015-06-01  65000.0                  8    1      6  2015
2       Eric Idle 2014-05-12  45000.0                 10   12      5  2014
3     Terry Jones 2013-11-01  70000.0                  3    1     11  2013
4   Terry Gilliam 2014-08-12  48000.0                  7   12      8  2014
5   Michael Palin 2013-05-23  66000.0                  8   23      5  2013


import pandas as pd 
df=pd.read_csv('eployee.csv1')
print(df)

    SNo    Name   Age       City  Country   Salary
0     1     Tom  28.0    Toronto   Canada  20000.0
1     2     Lee  31.0    Edmonto   Canada   3900.0
2     3    Dave  34.0    Toronto   Canada   8000.0
3     4    Anik  26.0   HongKong     Asia  66672.0
4     5   kamal  30.0        NaN  America      NaN
5     6    Geet   NaN   HongKong     Asia  30007.0
6     7  Steven  43.0    BayArea  America   8300.0
7     8     Ram  38.0  Hyderabad     Asia  54666.0
8     9    hari  50.0    newyork  America  67666.0
9    11    yami   NaN    newyork  America   8888.0
10   12    Anik  26.0   HongKong     Asia  66672.0
11   13   kamal  30.0        NaN  America  34344.0
12   14    Geet  22.0   HongKong     Asia  30007.0
13   15  Steven  43.0        NaN  America   8300.0
14   16     Ram  38.0  Hyderabad     Asia  54666.0
15   17    hari   NaN    newyork  America  67666.0
16   18    yami  27.0    newyork  America   8888.0
17   19     Lee  31.0    Edmonto   Canada      NaN
18   20  Andrew  34.0      Congo   Africa  34432.0
19   21   soham  32.0     Angola   Africa  34534.0
20   22    Grig  26.0    Finland   Europe  33451.0
21   23   Kiara  27.0     London   Europe      NaN


df1=pd.DataFrame(data=df,columns=['Name'])
df1


df1=pd.DataFrame(data=df,columns=['Name','Age','Salary'])
df1


 df1[['Name','Age','Salary']].head(10)


df.head()


df[df['City']=='newyork']


# select * from df where city=newyork and salary > 50000
df[(df['City']=='newyork')&(df['Salary']>50000)]


# select * from df where Country=America OR Salary > 50000
df[(df['Country']=='America')|(df['Salary']>50000)]


#Select * From Where column is Null:
df[df['Salary'].isna()]


#Select * From Where column is not Null:
df[df['Salary'].notna()]


df5=df.dropna(axis=0)
print(df5)

    SNo    Name   Age       City  Country   Salary
0     1     Tom  28.0    Toronto   Canada  20000.0
1     2     Lee  31.0    Edmonto   Canada   3900.0
2     3    Dave  34.0    Toronto   Canada   8000.0
3     4    Anik  26.0   HongKong     Asia  66672.0
6     7  Steven  43.0    BayArea  America   8300.0
7     8     Ram  38.0  Hyderabad     Asia  54666.0
8     9    hari  50.0    newyork  America  67666.0
10   12    Anik  26.0   HongKong     Asia  66672.0
12   14    Geet  22.0   HongKong     Asia  30007.0
14   16     Ram  38.0  Hyderabad     Asia  54666.0
16   18    yami  27.0    newyork  America   8888.0
18   20  Andrew  34.0      Congo   Africa  34432.0
19   21   soham  32.0     Angola   Africa  34534.0
20   22    Grig  26.0    Finland   Europe  33451.0


df.isnull().sum()

SNo        0
Name       0
Age        3
City       3
Country    0
Salary     3
dtype: int64


df.dropna(axis=0,inplace=True)


df.isnull().sum()

SNo        0
Name       0
Age        0
City       0
Country    0
Salary     0
dtype: int64

df


import pandas as pd

raw_data={'subject_id':['1','2','3','4','5'],
          'first_name':['Alex','Amy','Allen','Abdul','Halima'],
          'last_name':['Ali','Abdullahi','Faisal','Abdulaziz','Basit']}
df_a=pd.DataFrame(raw_data,columns=['subject_id','first_name','last_name'])
df_a


raw_data={'subject_id':['4','5','6','7','8'],
          'first_name':['Billy','Segun','Ali','Jamal','Sodiq'],
          'last_name':['Alimi','Basit','Fawaz','Kamal','Abdulyezid']}
df_b=pd.DataFrame(raw_data,columns=['subject_id','first_name','last_name'])
df_b


df_new=pd.concat([df_a,df_b],ignore_index=True)
df_new


raw_data={'subject_id':['1','2','3','4','5','6','7','8','9','10'],
          'test_id':[51,15,15,61,16,14,15,1,61,16]}
df_n=pd.DataFrame(raw_data,columns=['subject_id','test_id'])
df_n


pd.merge(df_n,df_new)


pd.merge(df_n,df_new, on='subject_id')


#merge with inner join
#inner join produces only the set of records that match in both table A and table b

pd.merge(df_a,df_b, on='subject_id', how='inner')


# outer join
pd.merge(df_a,df_b, on='subject_id', how='outer')


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings('ignore')


a=np.array([1,2,3,4,5,6])
b=np.array([2,3,4,5,6,7])

a

array([1, 2, 3, 4, 5, 6])

b

array([2, 3, 4, 5, 6, 7])


plt.title('Linear line')
plt.xlabel('a')
plt.ylabel('b')
plt.plot(a,b)
plt.show()


s=np.array([24,56,43,67,89,90,96,76])
p=np.array([12,24,56,90,87,90,45,34])

plt.title('Linear Line')
plt.xlabel('p')
plt.ylabel('s')
plt.plot(p,s)
plt.show()


a=np.array([1,2,3,4,5,6])
b=np.array([2,3,4,5,6,7])

plt.title('Linear line')
plt.xlabel('a')
plt.ylabel('b')
plt.plot(a,b,marker='*')
plt.show()


a=np.array([1,2,3,4,5,6])
b=np.array([2,3,4,5,6,7])

plt.title('Linear line')
plt.xlabel('a')
plt.ylabel('b')
plt.plot(a,b,marker='*',color='red')
plt.show()


a=np.array([1,2,3,4,5,6])
b=np.array([2,3,4,5,6,7])

plt.title('Linear line')
plt.xlabel('a')
plt.ylabel('b')
plt.plot(a,b,'--g')
plt.show()


help(plt.plot)

Help on function plot in module matplotlib.pyplot:

plot(*args, scalex=True, scaley=True, data=None, **kwargs)
    Plot y versus x as lines and/or markers.
    
    Call signatures::
    
        plot([x], y, [fmt], *, data=None, **kwargs)
        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)
    
    The coordinates of the points or line nodes are given by *x*, *y*.
    
    The optional parameter *fmt* is a convenient way for defining basic
    formatting like color, marker and linestyle. It's a shortcut string
    notation described in the *Notes* section below.
    
    >>> plot(x, y)        # plot x and y using default line style and color
    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers
    >>> plot(y)           # plot y using x as index array 0..N-1
    >>> plot(y, 'r+')     # ditto, but with red plusses
    
    You can use `.Line2D` properties as keyword arguments for more
    control on the appearance. Line properties and *fmt* can be mixed.
    The following two calls yield identical results:
    
    >>> plot(x, y, 'go--', linewidth=2, markersize=12)
    >>> plot(x, y, color='green', marker='o', linestyle='dashed',
    ...      linewidth=2, markersize=12)
    
    When conflicting with *fmt*, keyword arguments take precedence.
    
    
    **Plotting labelled data**
    
    There's a convenient way for plotting objects with labelled data (i.e.
    data that can be accessed by index ``obj['y']``). Instead of giving
    the data in *x* and *y*, you can provide the object in the *data*
    parameter and just give the labels for *x* and *y*::
    
    >>> plot('xlabel', 'ylabel', data=obj)
    
    All indexable objects are supported. This could e.g. be a `dict`, a
    `pandas.DataFrame` or a structured numpy array.
    
    
    **Plotting multiple sets of data**
    
    There are various ways to plot multiple sets of data.
    
    - The most straight forward way is just to call `plot` multiple times.
      Example:
    
      >>> plot(x1, y1, 'bo')
      >>> plot(x2, y2, 'go')
    
    - If *x* and/or *y* are 2D arrays a separate data set will be drawn
      for every column. If both *x* and *y* are 2D, they must have the
      same shape. If only one of them is 2D with shape (N, m) the other
      must have length N and will be used for every data set m.
    
      Example:
    
      >>> x = [1, 2, 3]
      >>> y = np.array([[1, 2], [3, 4], [5, 6]])
      >>> plot(x, y)
    
      is equivalent to:
    
      >>> for col in range(y.shape[1]):
      ...     plot(x, y[:, col])
    
    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*
      groups::
    
      >>> plot(x1, y1, 'g^', x2, y2, 'g-')
    
      In this case, any additional keyword argument applies to all
      datasets. Also this syntax cannot be combined with the *data*
      parameter.
    
    By default, each line is assigned a different style specified by a
    'style cycle'. The *fmt* and line property parameters are only
    necessary if you want explicit deviations from these defaults.
    Alternatively, you can also change the style cycle using
    :rc:`axes.prop_cycle`.
    
    
    Parameters
    ----------
    x, y : array-like or scalar
        The horizontal / vertical coordinates of the data points.
        *x* values are optional and default to ``range(len(y))``.
    
        Commonly, these parameters are 1D arrays.
    
        They can also be scalars, or two-dimensional (in that case, the
        columns represent separate data sets).
    
        These arguments cannot be passed as keywords.
    
    fmt : str, optional
        A format string, e.g. 'ro' for red circles. See the *Notes*
        section for a full description of the format strings.
    
        Format strings are just an abbreviation for quickly setting
        basic line properties. All of these and more can also be
        controlled by keyword arguments.
    
        This argument cannot be passed as keyword.
    
    data : indexable object, optional
        An object with labelled data. If given, provide the label names to
        plot in *x* and *y*.
    
        .. note::
            Technically there's a slight ambiguity in calls where the
            second label is a valid *fmt*. ``plot('n', 'o', data=obj)``
            could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,
            the former interpretation is chosen, but a warning is issued.
            You may suppress the warning by adding an empty format string
            ``plot('n', 'o', '', data=obj)``.
    
    Returns
    -------
    list of `.Line2D`
        A list of lines representing the plotted data.
    
    Other Parameters
    ----------------
    scalex, scaley : bool, default: True
        These parameters determine if the view limits are adapted to the
        data limits. The values are passed on to `autoscale_view`.
    
    **kwargs : `.Line2D` properties, optional
        *kwargs* are used to specify properties like a line label (for
        auto legends), linewidth, antialiasing, marker face color.
        Example::
    
        >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)
        >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')
    
        If you specify multiple lines with one plot call, the kwargs apply
        to all those lines. In case the label object is iterable, each
        element is used as labels for each set of data.
    
        Here is a list of available `.Line2D` properties:
    
        Properties:
        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
        alpha: scalar or None
        animated: bool
        antialiased or aa: bool
        clip_box: `.Bbox`
        clip_on: bool
        clip_path: Patch or (Path, Transform) or None
        color or c: color
        dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}
        dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}
        dashes: sequence of floats (on/off ink in points) or (None, None)
        data: (2, N) array or two 1D arrays
        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'
        figure: `.Figure`
        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}
        gid: str
        in_layout: bool
        label: object
        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
        linewidth or lw: float
        marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`
        markeredgecolor or mec: color
        markeredgewidth or mew: float
        markerfacecolor or mfc: color
        markerfacecoloralt or mfcalt: color
        markersize or ms: float
        markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]
        path_effects: `.AbstractPathEffect`
        picker: float or callable[[Artist, Event], tuple[bool, dict]]
        pickradius: float
        rasterized: bool
        sketch_params: (scale: float, length: float, randomness: float)
        snap: bool or None
        solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}
        solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}
        transform: unknown
        url: str
        visible: bool
        xdata: 1D array
        ydata: 1D array
        zorder: float
    
    See Also
    --------
    scatter : XY scatter plot with markers of varying size and/or color (
        sometimes also called bubble chart).
    
    Notes
    -----
    **Format Strings**
    
    A format string consists of a part for color, marker and line::
    
        fmt = '[marker][line][color]'
    
    Each of them is optional. If not provided, the value from the style
    cycle is used. Exception: If ``line`` is given, but no ``marker``,
    the data will be a line without markers.
    
    Other combinations such as ``[color][marker][line]`` are also
    supported, but note that their parsing may be ambiguous.
    
    **Markers**
    
    =============   ===============================
    character       description
    =============   ===============================
    ``'.'``         point marker
    ``','``         pixel marker
    ``'o'``         circle marker
    ``'v'``         triangle_down marker
    ``'^'``         triangle_up marker
    ``'<'``         triangle_left marker
    ``'>'``         triangle_right marker
    ``'1'``         tri_down marker
    ``'2'``         tri_up marker
    ``'3'``         tri_left marker
    ``'4'``         tri_right marker
    ``'8'``         octagon marker
    ``'s'``         square marker
    ``'p'``         pentagon marker
    ``'P'``         plus (filled) marker
    ``'*'``         star marker
    ``'h'``         hexagon1 marker
    ``'H'``         hexagon2 marker
    ``'+'``         plus marker
    ``'x'``         x marker
    ``'X'``         x (filled) marker
    ``'D'``         diamond marker
    ``'d'``         thin_diamond marker
    ``'|'``         vline marker
    ``'_'``         hline marker
    =============   ===============================
    
    **Line Styles**
    
    =============    ===============================
    character        description
    =============    ===============================
    ``'-'``          solid line style
    ``'--'``         dashed line style
    ``'-.'``         dash-dot line style
    ``':'``          dotted line style
    =============    ===============================
    
    Example format strings::
    
        'b'    # blue markers with default shape
        'or'   # red circles
        '-g'   # green solid line
        '--'   # dashed line with default color
        '^k:'  # black triangle_up markers connected by a dotted line
    
    **Colors**
    
    The supported color abbreviations are the single letter codes
    
    =============    ===============================
    character        color
    =============    ===============================
    ``'b'``          blue
    ``'g'``          green
    ``'r'``          red
    ``'c'``          cyan
    ``'m'``          magenta
    ``'y'``          yellow
    ``'k'``          black
    ``'w'``          white
    =============    ===============================
    
    and the ``'CN'`` colors that index into the default property cycle.
    
    If the color is the only part of the format string, you can
    additionally use any  `matplotlib.colors` spec, e.g. full names
    (``'green'``) or hex strings (``'#008000'``).


a=np.array([1,2,3,4,5,6])
b=np.array([2,3,4,5,6,7])

plt.title('Linear line')
plt.xlabel('a')
plt.ylabel('b')
plt.plot(a,b,'bo')
plt.show()


a=np.array([1,2,3,4,5,6])
b=np.array([2,3,4,5,6,7])

plt.title('Linear line')
plt.xlabel('a')
plt.ylabel('b')
plt.plot(a,b,'r+')
plt.show()


a=np.array([1,2,3,4,5,6])
b=np.array([2,3,4,5,6,7])

plt.title('Linear line')
plt.xlabel('a')
plt.ylabel('b')
plt.plot(a,b,color='green',marker='o',linestyle='dashed')
plt.show()


x=[12,13,14,15,16]
y=[77,33,22,44,55]

plt.title('list')
plt.xlabel('x')
plt.ylabel('y')
plt.plot(x,y,marker='o',color='green')
plt.show()


sales=[100,200,300,400,500]
fruitname=['Apple','Mango','Kiwi','Grapes','papaya']

plt.title('Sales ')
plt.xlabel('sales')
plt.ylabel('fruitname')
plt.plot(sales,fruitname,marker='o',color='blue')
plt.show()


sales=[100,200,300,400,500]
fruitname=['Apple','Mango','Kiwi','Grapes','papaya']

plt.title('Sales ')
plt.xlabel('fruit')
plt.ylabel('sale')
plt.bar(fruitname,sales,color='green',align='edge')
plt.show()


a=np.array([12,10,22,34,35,37,40,55,54,62,68,69,89,83,92])
plt.hist(a,bins=[0,10,20,30,40,50,60,70,80,90,100])
plt.title('histogram')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


import matplotlib.pyplot as plt
import pandas as pd
df=pd.read_csv('student_mark.csv')
print(df)
df.columns

  Unnamed: 0 Gender         DOB  Maths  Physics  Chemistry  English  Biology  \
0       John      M  05-04-1988     55       45         56       87       21   
1     Suresh      M  04-05-1987     75       96         78       64       90   
2     Ramesh      M  25-05-1989     25       54         89       76       95   
3    Jessica      F  12-08-1990     78       96         86       63       54   
4   Jennifer      F  02-09-1989     58       96         78       46       96   
5       Annu      F  05-04-1988     45       87         52       89       55   
6      pooja      F  04-05-1987     55       64         61       58       75   
7     Ritesh      M  25-05-1989     54       76         87       56       25   
8      Farha      F  12-08-1990     55       63         89       75       78   
9     Mukesh      M  02-09-1989     96       46         77       83       58   

   Economics  History  Civics  
0         52       89      65  
1         61       58       2  
2         87       56      74  
3         89       75      45  
4         77       83      53  
5         89       87      52  
6         58       64      61  
7         56       76      87  
8         75       63      89  
9         83       46      77

Index(['Unnamed: 0', 'Gender', 'DOB', 'Maths', 'Physics', 'Chemistry',
       'English', 'Biology', 'Economics', 'History', 'Civics'],
      dtype='object')


plt.hist(df['Maths'],bins=[0,20,40,60,80,100])
plt.title('Maths Scores')
plt.xlabel('Maths')
plt.ylabel('Frequency')
plt.show()


plt.boxplot(df['Maths'])

{'whiskers': [<matplotlib.lines.Line2D at 0x29a8cf999d0>,
  <matplotlib.lines.Line2D at 0x29a8cf99430>],
 'caps': [<matplotlib.lines.Line2D at 0x29a8cfc9280>,
  <matplotlib.lines.Line2D at 0x29a8cfc9850>],
 'boxes': [<matplotlib.lines.Line2D at 0x29a8cf99940>],
 'medians': [<matplotlib.lines.Line2D at 0x29a8cfc9730>],
 'fliers': [<matplotlib.lines.Line2D at 0x29a8cfc90a0>],
 'means': []}


plt.boxplot(df['Chemistry'])

{'whiskers': [<matplotlib.lines.Line2D at 0x29a8e3aad60>,
  <matplotlib.lines.Line2D at 0x29a8e3aa8e0>],
 'caps': [<matplotlib.lines.Line2D at 0x29a8e3aad90>,
  <matplotlib.lines.Line2D at 0x29a8e3aae80>],
 'boxes': [<matplotlib.lines.Line2D at 0x29a8d139c10>],
 'medians': [<matplotlib.lines.Line2D at 0x29a8e3aa4f0>],
 'fliers': [<matplotlib.lines.Line2D at 0x29a8e3aa6d0>],
 'means': []}


plt.violinplot(df['Maths'])

{'bodies': [<matplotlib.collections.PolyCollection at 0x29a8ceb45b0>],
 'cmaxes': <matplotlib.collections.LineCollection at 0x29a8ceb4e80>,
 'cmins': <matplotlib.collections.LineCollection at 0x29a8ceb4a00>,
 'cbars': <matplotlib.collections.LineCollection at 0x29a8ceb4250>}


plt.violinplot(df['Chemistry'])

{'bodies': [<matplotlib.collections.PolyCollection at 0x29a8e78d3d0>],
 'cmaxes': <matplotlib.collections.LineCollection at 0x29a8e78d520>,
 'cmins': <matplotlib.collections.LineCollection at 0x29a8e5d9e20>,
 'cbars': <matplotlib.collections.LineCollection at 0x29a8e5d9d00>}


s=np.array([24,56,43,67,89,90,96,76])
p=np.array([12,24,56,90,87,90,45,34])

plt.title('scatter plot')
plt.xlabel('p')
plt.ylabel('s')
plt.scatter(p,s)
plt.show()


sales=[100,200,300,400,500]
fruitname=['Apple','Mango','Kiwi','Grapes','papaya']

plt.title('Sales ')
plt.xlabel('fruit')
plt.ylabel('sale')
plt.scatter(fruitname,sales)
plt.show()


x=df['Maths']
y=df['English']


plt.scatter(x,y)
plt.xlabel('Maths')
plt.ylabel('Engish')
plt.show()


df2=pd.read_csv('Head_Brain_csv')
print(df2)
df2.columns

     Gender  AgeRange  HeadSize  BrainWeight
0         1         1      4512         1530
1         1         1      3738         1297
2         1         1      4261         1335
3         1         1      3777         1282
4         1         1      4177         1590
..      ...       ...       ...          ...
232       2         2      3214         1110
233       2         2      3394         1215
234       2         2      3233         1104
235       2         2      3352         1170
236       2         2      3391         1120

[237 rows x 4 columns]

Index(['Gender', 'AgeRange', 'HeadSize', 'BrainWeight'], dtype='object')


df2['HeadSize']

0      4512
1      3738
2      4261
3      3777
4      4177
       ... 
232    3214
233    3394
234    3233
235    3352
236    3391
Name: HeadSize, Length: 237, dtype: int64


df2['BrainWeight']

0      1530
1      1297
2      1335
3      1282
4      1590
       ... 
232    1110
233    1215
234    1104
235    1170
236    1120
Name: BrainWeight, Length: 237, dtype: int64


x=df2['HeadSize']
y=df2['BrainWeight']

plt.scatter(x,y)
plt.xlabel('HeadSize')
plt.ylabel('BrainWeight')
plt.show()


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import warnings
warnings.filterwarnings('ignore')


a=np.array([1,2,3,4,5,6])
b=np.array([2,3,4,5,6,7])


sns.stripplot(a,b)

<AxesSubplot:>


df4=pd.read_csv('Head_Brain_csv')
df4


sns.violinplot?


sns.violinplot(x='BrainWeight',data=df4)

<AxesSubplot:xlabel='BrainWeight'>


sns.violinplot(x='AgeRange',data=df4)

<AxesSubplot:xlabel='AgeRange'>


import numpy as np
import pandas as pd
import seaborn as sns

import warnings
warnings.filterwarnings('ignore')


df=pd.read_csv('eployee.csv1')
df


sns.violinplot(x='Age',data=df)

<AxesSubplot:xlabel='Age'>


sns.violinplot(x='Salary',data=df)

<AxesSubplot:xlabel='Salary'>


df2=pd.read_csv('Head_Brain_csv')
df2


sns.violinplot(x='BrainWeight',data=df2)

<AxesSubplot:xlabel='BrainWeight'>


dfiris=pd.read_csv('iris.csv')
dfiris


sns.stripplot(x='class',y='sepal length',data=dfiris)

<AxesSubplot:xlabel='class', ylabel='sepal length'>


sns.stripplot(x='class',y='petal length',data=dfiris)

<AxesSubplot:xlabel='class', ylabel='petal length'>


sns.stripplot(x='class',y='sepal width',data=dfiris)

<AxesSubplot:xlabel='class', ylabel='sepal width'>


sns.stripplot(x='class',y='petal width',data=dfiris)

<AxesSubplot:xlabel='class', ylabel='petal width'>


sns.swarmplot(x='class',y='petal width',data=dfiris)

<AxesSubplot:xlabel='class', ylabel='petal width'>


sns.swarmplot(x='class',y='sepal length',data=dfiris)

<AxesSubplot:xlabel='class', ylabel='sepal length'>


dfiris


dfiris['class'].value_counts()

Iris-setosa        50
Iris-versicolor    50
Iris-virginica     50
Name: class, dtype: int64


sns.countplot(dfiris['class'])

<AxesSubplot:xlabel='class', ylabel='count'>


sns.get_dataset_names()

['anagrams',
 'anscombe',
 'attention',
 'brain_networks',
 'car_crashes',
 'diamonds',
 'dots',
 'dowjones',
 'exercise',
 'flights',
 'fmri',
 'geyser',
 'glue',
 'healthexp',
 'iris',
 'mpg',
 'penguins',
 'planets',
 'seaice',
 'taxis',
 'tips',
 'titanic',
 'anagrams',
 'anagrams',
 'anscombe',
 'anscombe',
 'attention',
 'attention',
 'brain_networks',
 'brain_networks',
 'car_crashes',
 'car_crashes',
 'diamonds',
 'diamonds',
 'dots',
 'dots',
 'dowjones',
 'dowjones',
 'exercise',
 'exercise',
 'flights',
 'flights',
 'fmri',
 'fmri',
 'geyser',
 'geyser',
 'glue',
 'glue',
 'healthexp',
 'healthexp',
 'iris',
 'iris',
 'mpg',
 'mpg',
 'penguins',
 'penguins',
 'planets',
 'planets',
 'seaice',
 'seaice',
 'taxis',
 'taxis',
 'tips',
 'tips',
 'titanic',
 'titanic',
 'anagrams',
 'anscombe',
 'attention',
 'brain_networks',
 'car_crashes',
 'diamonds',
 'dots',
 'dowjones',
 'exercise',
 'flights',
 'fmri',
 'geyser',
 'glue',
 'healthexp',
 'iris',
 'mpg',
 'penguins',
 'planets',
 'seaice',
 'taxis',
 'tips',
 'titanic']


titanicds=sns.load_dataset('titanic')
titanicds


titanicds.columns

Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',
       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',
       'alive', 'alone'],
      dtype='object')


titanicds['class']

0       Third
1       First
2       Third
3       First
4       Third
        ...  
886    Second
887     First
888     Third
889     First
890     Third
Name: class, Length: 891, dtype: category
Categories (3, object): ['First', 'Second', 'Third']


titanicds['class'].value_counts()

Third     491
First     216
Second    184
Name: class, dtype: int64


sns.countplot(titanicds['class'])

<AxesSubplot:xlabel='class', ylabel='count'>


sns.catplot(x="class",y="survived",data=titanicds,kind="bar")

<seaborn.axisgrid.FacetGrid at 0x1c96b9256a0>


sns.catplot(x="class",y="survived",hue="sex",data=titanicds,kind="bar")

<seaborn.axisgrid.FacetGrid at 0x1c96ca6b790>


help(sns.catplot)

Help on function catplot in module seaborn.categorical:

catplot(*, x=None, y=None, hue=None, data=None, row=None, col=None, col_wrap=None, estimator=<function mean at 0x000001C965F9F5E0>, ci=95, n_boot=1000, units=None, seed=None, order=None, hue_order=None, row_order=None, col_order=None, kind='strip', height=5, aspect=1, orient=None, color=None, palette=None, legend=True, legend_out=True, sharex=True, sharey=True, margin_titles=False, facet_kws=None, **kwargs)
    Figure-level interface for drawing categorical plots onto a FacetGrid.
    
    This function provides access to several axes-level functions that
    show the relationship between a numerical and one or more categorical
    variables using one of several visual representations. The ``kind``
    parameter selects the underlying axes-level function to use:
    
    Categorical scatterplots:
    
    - :func:`stripplot` (with ``kind="strip"``; the default)
    - :func:`swarmplot` (with ``kind="swarm"``)
    
    Categorical distribution plots:
    
    - :func:`boxplot` (with ``kind="box"``)
    - :func:`violinplot` (with ``kind="violin"``)
    - :func:`boxenplot` (with ``kind="boxen"``)
    
    Categorical estimate plots:
    
    - :func:`pointplot` (with ``kind="point"``)
    - :func:`barplot` (with ``kind="bar"``)
    - :func:`countplot` (with ``kind="count"``)
    
    Extra keyword arguments are passed to the underlying function, so you
    should refer to the documentation for each to see kind-specific options.
    
    Note that unlike when using the axes-level functions directly, data must be
    passed in a long-form DataFrame with variables specified by passing strings
    to ``x``, ``y``, ``hue``, etc.
    
    As in the case with the underlying plot functions, if variables have a
    ``categorical`` data type, the levels of the categorical variables, and
    their order will be inferred from the objects. Otherwise you may have to
    use alter the dataframe sorting or use the function parameters (``orient``,
    ``order``, ``hue_order``, etc.) to set up the plot correctly.
    
    This function always treats one of the variables as categorical and
    draws data at ordinal positions (0, 1, ... n) on the relevant axis, even
    when the data has a numeric or date type.
    
    See the :ref:`tutorial <categorical_tutorial>` for more information.    
    
    After plotting, the :class:`FacetGrid` with the plot is returned and can
    be used directly to tweak supporting plot details or add other layers.
    
    Parameters
    ----------
    x, y, hue : names of variables in ``data``
        Inputs for plotting long-form data. See examples for interpretation.        
    data : DataFrame
        Long-form (tidy) dataset for plotting. Each column should correspond
        to a variable, and each row should correspond to an observation.    
    row, col : names of variables in ``data``, optional
        Categorical variables that will determine the faceting of the grid.
    col_wrap : int
        "Wrap" the column variable at this width, so that the column facets
        span multiple rows. Incompatible with a ``row`` facet.    
    estimator : callable that maps vector -> scalar, optional
        Statistical function to estimate within each categorical bin.
    ci : float or "sd" or None, optional
        Size of confidence intervals to draw around estimated values.  If
        "sd", skip bootstrapping and draw the standard deviation of the
        observations. If ``None``, no bootstrapping will be performed, and
        error bars will not be drawn.
    n_boot : int, optional
        Number of bootstrap iterations to use when computing confidence
        intervals.
    units : name of variable in ``data`` or vector data, optional
        Identifier of sampling units, which will be used to perform a
        multilevel bootstrap and account for repeated measures design.
    seed : int, numpy.random.Generator, or numpy.random.RandomState, optional
        Seed or random number generator for reproducible bootstrapping.    
    order, hue_order : lists of strings, optional
        Order to plot the categorical levels in, otherwise the levels are
        inferred from the data objects.        
    row_order, col_order : lists of strings, optional
        Order to organize the rows and/or columns of the grid in, otherwise the
        orders are inferred from the data objects.
    kind : str, optional
        The kind of plot to draw, corresponds to the name of a categorical
        axes-level plotting function. Options are: "strip", "swarm", "box", "violin",
        "boxen", "point", "bar", or "count".
    height : scalar
        Height (in inches) of each facet. See also: ``aspect``.    
    aspect : scalar
        Aspect ratio of each facet, so that ``aspect * height`` gives the width
        of each facet in inches.    
    orient : "v" | "h", optional
        Orientation of the plot (vertical or horizontal). This is usually
        inferred based on the type of the input variables, but it can be used
        to resolve ambiguity when both `x` and `y` are numeric or when
        plotting wide-form data.    
    color : matplotlib color, optional
        Color for all of the elements, or seed for a gradient palette.    
    palette : palette name, list, or dict
        Colors to use for the different levels of the ``hue`` variable. Should
        be something that can be interpreted by :func:`color_palette`, or a
        dictionary mapping hue levels to matplotlib colors.    
    legend : bool, optional
        If ``True`` and there is a ``hue`` variable, draw a legend on the plot.
    legend_out : bool
        If ``True``, the figure size will be extended, and the legend will be
        drawn outside the plot on the center right.    
    share{x,y} : bool, 'col', or 'row' optional
        If true, the facets will share y axes across columns and/or x axes
        across rows.    
    margin_titles : bool
        If ``True``, the titles for the row variable are drawn to the right of
        the last column. This option is experimental and may not work in all
        cases.    
    facet_kws : dict, optional
        Dictionary of other keyword arguments to pass to :class:`FacetGrid`.
    kwargs : key, value pairings
        Other keyword arguments are passed through to the underlying plotting
        function.
    
    Returns
    -------
    g : :class:`FacetGrid`
        Returns the :class:`FacetGrid` object with the plot on it for further
        tweaking.
    
    Examples
    --------
    
    Draw a single facet to use the :class:`FacetGrid` legend placement:
    
    .. plot::
        :context: close-figs
    
        >>> import seaborn as sns
        >>> sns.set_theme(style="ticks")
        >>> exercise = sns.load_dataset("exercise")
        >>> g = sns.catplot(x="time", y="pulse", hue="kind", data=exercise)
    
    Use a different plot kind to visualize the same data:
    
    .. plot::
        :context: close-figs
    
        >>> g = sns.catplot(x="time", y="pulse", hue="kind",
        ...                data=exercise, kind="violin")
    
    Facet along the columns to show a third categorical variable:
    
    .. plot::
        :context: close-figs
    
        >>> g = sns.catplot(x="time", y="pulse", hue="kind",
        ...                 col="diet", data=exercise)
    
    Use a different height and aspect ratio for the facets:
    
    .. plot::
        :context: close-figs
    
        >>> g = sns.catplot(x="time", y="pulse", hue="kind",
        ...                 col="diet", data=exercise,
        ...                 height=5, aspect=.8)
    
    Make many column facets and wrap them into the rows of the grid:
    
    .. plot::
        :context: close-figs
    
        >>> titanic = sns.load_dataset("titanic")
        >>> g = sns.catplot(x="alive", col="deck", col_wrap=4,
        ...                 data=titanic[titanic.deck.notnull()],
        ...                 kind="count", height=2.5, aspect=.8)
    
    Plot horizontally and pass other keyword arguments to the plot function:
    
    .. plot::
        :context: close-figs
    
        >>> g = sns.catplot(x="age", y="embark_town",
        ...                 hue="sex", row="class",
        ...                 data=titanic[titanic.embark_town.notnull()],
        ...                 orient="h", height=2, aspect=3, palette="Set3",
        ...                 kind="violin", dodge=True, cut=0, bw=.2)
    
    Use methods on the returned :class:`FacetGrid` to tweak the presentation:
    
    .. plot::
        :context: close-figs
    
        >>> g = sns.catplot(x="who", y="survived", col="class",
        ...                 data=titanic, saturation=.5,
        ...                 kind="bar", ci=None, aspect=.6)
        >>> (g.set_axis_labels("", "Survival Rate")
        ...   .set_xticklabels(["Men", "Women", "Children"])
        ...   .set_titles("{col_name} {col_var}")
        ...   .set(ylim=(0, 1))
        ...   .despine(left=True))  #doctest: +ELLIPSIS
        <seaborn.axisgrid.FacetGrid object at 0x...>


sns.catplot?


sns.catplot(x="class",y="survived",hue="sex",data=titanicds,kind="violin")

<seaborn.axisgrid.FacetGrid at 0x1c96e34d4c0>


sns.catplot(x="class",y="survived",hue="sex",data=titanicds,kind="strip")

<seaborn.axisgrid.FacetGrid at 0x1c96e40d0d0>


tipsds=sns.load_dataset('tips')
tipsds


tipsds=sns.violinplot(x='total_bill',data=tipsds)
tipsds

<AxesSubplot:xlabel='total_bill'>


sns.violinplot(x='tip',data=tipsds)

<AxesSubplot:xlabel='tip'>


tipsds['day'].value_counts()

Sat     87
Sun     76
Thur    62
Fri     19
Name: day, dtype: int64


sns.countplot(tipsds['day'])

<AxesSubplot:xlabel='day', ylabel='count'>


tipsds['smoker'].value_counts()

No     151
Yes     93
Name: smoker, dtype: int64


sns.countplot(tipsds['smoker'])

<AxesSubplot:xlabel='smoker', ylabel='count'>


sns.boxplot(tipsds['total_bill'])

<AxesSubplot:xlabel='total_bill'>


sns.histplot(data=tipsds,x='total_bill',bins=20)

<AxesSubplot:xlabel='total_bill', ylabel='Count'>


sns.histplot(data=tipsds,x='tip',bins=20)

<AxesSubplot:xlabel='tip', ylabel='Count'>


sns.distplot(tipsds['tip'],bins=10)

<AxesSubplot:xlabel='tip', ylabel='Density'>


sns.distplot(tipsds['total_bill'],kde=False)

<AxesSubplot:xlabel='total_bill'>


sns.distplot(tipsds['total_bill'],kde=True)

<AxesSubplot:xlabel='total_bill', ylabel='Density'>


sns.kdeplot(tipsds['total_bill'])

<AxesSubplot:xlabel='total_bill', ylabel='Density'>


sns.jointplot(data=tipsds,x='total_bill',y='tip')

<seaborn.axisgrid.JointGrid at 0x1c96fc18f40>


sns.rugplot(data=tipsds,x='tip')

<AxesSubplot:xlabel='tip'>


sns.rugplot(data=tipsds,x='total_bill',y='tip')

<AxesSubplot:xlabel='total_bill', ylabel='tip'>


tipsds


sns.boxplot(x='day',y='total_bill',data=tipsds)

<AxesSubplot:xlabel='day', ylabel='total_bill'>


sns.boxplot(x='day',y='total_bill',hue='sex',data=tipsds)

<AxesSubplot:xlabel='day', ylabel='total_bill'>


sns.boxplot(x='day',y='total_bill',hue='smoker',data=tipsds)

<AxesSubplot:xlabel='day', ylabel='total_bill'>


sns.pairplot(tipsds)

<seaborn.axisgrid.PairGrid at 0x1c970137520>


sns.scatterplot(x='total_bill',y='tip',data=tipsds)

<AxesSubplot:xlabel='total_bill', ylabel='tip'>


mpgds=sns.load_dataset('mpg')
mpgds


sns.scatterplot(data=mpgds,x='mpg',y='weight')

<AxesSubplot:xlabel='mpg', ylabel='weight'>


sns.relplot(data=mpgds,x='mpg',y='weight')

<seaborn.axisgrid.FacetGrid at 0x1c971d938b0>


sns.lineplot(data=mpgds,x='mpg',y='weight')

<AxesSubplot:xlabel='mpg', ylabel='weight'>


sns.boxplot(x='weight',data=mpgds)

<AxesSubplot:xlabel='weight'>


sns.boxplot(x='displacement',data=mpgds)

<AxesSubplot:xlabel='displacement'>


sns.rugplot(x='weight',y='mpg',data=mpgds)

<AxesSubplot:xlabel='weight', ylabel='mpg'>


sns.jointplot(data=mpgds,x='weight',y='mpg')

<seaborn.axisgrid.JointGrid at 0x1c972021580>


sns.jointplot(data=mpgds,x='weight',y='mpg',kind='kde')

<seaborn.axisgrid.JointGrid at 0x1c972115fa0>


sns.jointplot(data=mpgds,x='weight',y='mpg',kind='hex')

<seaborn.axisgrid.JointGrid at 0x1c971a7d760>


sns.stripplot(data=mpgds,x='origin',y='mpg')

<AxesSubplot:xlabel='origin', ylabel='mpg'>


sns.swarmplot(data=mpgds,x='origin',y='mpg')

<AxesSubplot:xlabel='origin', ylabel='mpg'>


sns.catplot(data=mpgds,x='origin',y='mpg',kind='box')

<seaborn.axisgrid.FacetGrid at 0x1c974308f70>


sns.get_dataset_names()

['anagrams',
 'anscombe',
 'attention',
 'brain_networks',
 'car_crashes',
 'diamonds',
 'dots',
 'dowjones',
 'exercise',
 'flights',
 'fmri',
 'geyser',
 'glue',
 'healthexp',
 'iris',
 'mpg',
 'penguins',
 'planets',
 'seaice',
 'taxis',
 'tips',
 'titanic',
 'anagrams',
 'anagrams',
 'anscombe',
 'anscombe',
 'attention',
 'attention',
 'brain_networks',
 'brain_networks',
 'car_crashes',
 'car_crashes',
 'diamonds',
 'diamonds',
 'dots',
 'dots',
 'dowjones',
 'dowjones',
 'exercise',
 'exercise',
 'flights',
 'flights',
 'fmri',
 'fmri',
 'geyser',
 'geyser',
 'glue',
 'glue',
 'healthexp',
 'healthexp',
 'iris',
 'iris',
 'mpg',
 'mpg',
 'penguins',
 'penguins',
 'planets',
 'planets',
 'seaice',
 'seaice',
 'taxis',
 'taxis',
 'tips',
 'tips',
 'titanic',
 'titanic',
 'anagrams',
 'anscombe',
 'attention',
 'brain_networks',
 'car_crashes',
 'diamonds',
 'dots',
 'dowjones',
 'exercise',
 'flights',
 'fmri',
 'geyser',
 'glue',
 'healthexp',
 'iris',
 'mpg',
 'penguins',
 'planets',
 'seaice',
 'taxis',
 'tips',
 'titanic']


taxi=sns.load_dataset('taxis')
taxi


sns.scatterplot(x='fare',y='distance',data=taxi)

<AxesSubplot:xlabel='fare', ylabel='distance'>


taxi['color'].value_counts()

yellow    5451
green      982
Name: color, dtype: int64


sns.countplot(x='color',data=taxi)

<AxesSubplot:xlabel='color', ylabel='count'>


sns.stripplot(data=taxi,x='color',y='fare')

<AxesSubplot:xlabel='color', ylabel='fare'>


sns.boxplot(data=taxi,x='color',y='fare',hue='payment')

<AxesSubplot:xlabel='color', ylabel='fare'>


sns.boxplot(data=taxi,x='color',y='fare',hue='payment')

<AxesSubplot:xlabel='color', ylabel='fare'>


sns.lmplot('distance','fare',data=taxi,hue='payment')

<seaborn.axisgrid.FacetGrid at 0x1c9743a5fd0>


sns.lmplot('distance','fare',data=taxi,hue='payment',col='color')

<seaborn.axisgrid.FacetGrid at 0x1c974826250>


flight=sns.load_dataset('flights')
flight


import seaborn as sns
flight=flight.pivot('month','year','passengers')
flight


ax=sns.heatmap(flight)
ax

<AxesSubplot:xlabel='year', ylabel='month'>


import numpy as np
import pandas as pd


a=np.array([24,65,87,98,90,45,70,80,64,90])
a

array([24, 65, 87, 98, 90, 45, 70, 80, 64, 90])


np.mean(a)

71.3


np.median(a)

75.0


np.max(a)

98


np.min(a)

24


rang=np.max(a)-np.min(a)
rang

74


#Variance
avar=np.var(a)
avar

475.81000000000006


#standard deviation
np.std(a)

21.813069476806792

a

array([24, 65, 87, 98, 90, 45, 70, 80, 64, 90])


# Percentile
np.percentile(a,60)

82.8


np.percentile(a,40)

68.0


np.percentile(a,75)

89.25


df=pd.read_csv('student_mark.csv')
df


df['Maths']

0    55
1    75
2    25
3    78
4    58
5    45
6    55
7    54
8    55
9    96
Name: Maths, dtype: int64


df['Maths'].mean()

59.6


df['Maths'].median()

55.0


df['Chemistry'].mean()

75.3


df['Chemistry'].median()

78.0


df['Chemistry'].mode()

0    78
1    89
Name: Chemistry, dtype: int64


df['Maths'].max()

96


df['Maths'].min()

25


rang=df['Maths'].max()-df['Maths'].min()
rang

71


mathv=df['Maths'].var()
mathv

378.71111111111105


e=df['Maths'].std()
e

19.46050130677807


import scipy as st
import warnings
warnings.filterwarnings('ignore')

a

array([24, 65, 87, 98, 90, 45, 70, 80, 64, 90])


st.mean(a)

71.3


st.median(a)

75.0


d=st.var(a)
d

475.81000000000006


round(d,2)

475.81


round(234567.0987654,3)

234567.099


import scipy.stats as st


b=np.array([4,4,5,46,4,3,24,67,543,43,234,54,32,23,5,6,7,7,8,88,8,8,8,8,8,8,8,8,8,8,8,7,65,4,3,2,2,])
b

array([  4,   4,   5,  46,   4,   3,  24,  67, 543,  43, 234,  54,  32,
        23,   5,   6,   7,   7,   8,  88,   8,   8,   8,   8,   8,   8,
         8,   8,   8,   8,   8,   7,  65,   4,   3,   2,   2])


st.mode(b)

ModeResult(mode=array([8]), count=array([12]))

df


df.describe()


import scipy
from scipy import stats
from scipy.stats import percentileofscore


percentileofscore(df['Maths'],25)

10.0


percentileofscore(df['Maths'],60)

70.0


percentileofscore(df['English'],75)

60.0


q1=df['Maths'].quantile(0.25)#25
q1

54.25


#variance 
df['Maths'].std()

19.46050130677807


#standard deviation
df['Maths'].std()

19.46050130677807


print(df['English'].var())
print(df['English'].std())

208.89999999999998
14.453373308677804


#to call out outliers or abnormal data 
from scipy.stats import zscore


zscore([43,45,67,87,87,28,90,56,])

array([-0.90075228, -0.81011054,  0.18694859,  1.09336597,  1.09336597,
       -1.58056532,  1.22932858, -0.31158098])


import pandas as pd


from scipy import stats


data=pd.read_csv('brain_size.csv',sep=';',na_values=".")
data


data['VIQ'].mean()

112.35


data[data['Gender']=='Female']['VIQ'].mean()

109.45


stats.ttest_1samp(data['PIQ'],106)

Ttest_1sampResult(statistic=1.4143037747013467, pvalue=0.16520690047123543)


stats.ttest_1samp(data['PIQ'],108)

Ttest_1sampResult(statistic=0.8513967996958363, pvalue=0.39974665151472855)


female_viq=data[data['Gender']=='Female']['VIQ']
male_viq=data[data['Gender']=='Male']['VIQ']


data['FSIQ'].mean()

113.45


data['PIQ'].mean()

111.025


stats.ttest_ind(data['FSIQ'],data['PIQ'])

Ttest_indResult(statistic=0.465637596380964, pvalue=0.6427725009414841)


df=pd.read_csv('student_mark.csv')
df


df.iloc[:,3:-1]


#correlation
dfc=df.corr()
dfc


import seaborn as sns
sns.heatmap(dfc)

<AxesSubplot:>


sns.heatmap(dfc,cmap='Blues',annot=True)

<AxesSubplot:>


import numpy as np
import pandas as pd
import scipy.stats as stats


#ds=pd.read_csv('')


from scipy.stats import f_oneway

marks_sectionA=[70,80,85,70,90]
marks_sectionB=[90,70,40,30,15]
marks_sectionc=[50,89,36,70,49]

f_oneway(marks_sectionA,marks_sectionB,marks_sectionc)

F_onewayResult(statistic=2.4330768697580933, pvalue=0.1297152831419187)


import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt


df=pd.read_csv('president._heights.csv')
df


df.columns

Index(['order', 'name', 'height'], dtype='object')


plt.boxplot(df.height)

{'whiskers': [<matplotlib.lines.Line2D at 0x1c969009c40>,
  <matplotlib.lines.Line2D at 0x1c969009f10>],
 'caps': [<matplotlib.lines.Line2D at 0x1c969027220>,
  <matplotlib.lines.Line2D at 0x1c9690274f0>],
 'boxes': [<matplotlib.lines.Line2D at 0x1c969009970>],
 'medians': [<matplotlib.lines.Line2D at 0x1c9690277c0>],
 'fliers': [<matplotlib.lines.Line2D at 0x1c969027a90>],
 'means': []}


# no outliers
df['height'].quantile([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1])

0.0    163.0
0.1    170.1
0.2    173.0
0.3    175.6
0.4    178.0
0.5    182.0
0.6    183.0
0.7    183.0
0.8    185.0
0.9    188.0
1.0    193.0
Name: height, dtype: float64


df2=pd.read_csv('HR_attriition_rate.csv')
df2.head()


df2.columns

Index(['satisfaction_level', 'last_evaluation', 'number_project',
       'average_montly_hours', 'time_spend_company', 'Work_accident', 'left',
       'promotion_last_5years', 'Departments ', 'salary'],
      dtype='object')


plt.boxplot(df2.satisfaction_level)

{'whiskers': [<matplotlib.lines.Line2D at 0x1c969258d90>,
  <matplotlib.lines.Line2D at 0x1c9690680a0>],
 'caps': [<matplotlib.lines.Line2D at 0x1c969068370>,
  <matplotlib.lines.Line2D at 0x1c969068670>],
 'boxes': [<matplotlib.lines.Line2D at 0x1c969258a90>],
 'medians': [<matplotlib.lines.Line2D at 0x1c969068940>],
 'fliers': [<matplotlib.lines.Line2D at 0x1c969068c10>],
 'means': []}


df2['satisfaction_level'].quantile([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1])

0.0    0.09
0.1    0.21
0.2    0.40
0.3    0.49
0.4    0.57
0.5    0.64
0.6    0.72
0.7    0.78
0.8    0.85
0.9    0.92
1.0    1.00
Name: satisfaction_level, dtype: float64


df3=pd.read_csv('brain_size.csv',sep=';')
df3.head()


df3.columns

Index(['id', 'Gender', 'FSIQ', 'VIQ', 'PIQ', 'Weight', 'Height', 'MRI_Count'], dtype='object')


plt.boxplot(df3.MRI_Count)

{'whiskers': [<matplotlib.lines.Line2D at 0x1c9690d0070>,
  <matplotlib.lines.Line2D at 0x1c9690d02e0>],
 'caps': [<matplotlib.lines.Line2D at 0x1c9690d05b0>,
  <matplotlib.lines.Line2D at 0x1c9690d0880>],
 'boxes': [<matplotlib.lines.Line2D at 0x1c9690c1d60>],
 'medians': [<matplotlib.lines.Line2D at 0x1c9690d0b50>],
 'fliers': [<matplotlib.lines.Line2D at 0x1c9690d0e20>],
 'means': []}


plt.boxplot(df3.FSIQ)

{'whiskers': [<matplotlib.lines.Line2D at 0x1c9691383d0>,
  <matplotlib.lines.Line2D at 0x1c9691386a0>],
 'caps': [<matplotlib.lines.Line2D at 0x1c969138970>,
  <matplotlib.lines.Line2D at 0x1c969138c40>],
 'boxes': [<matplotlib.lines.Line2D at 0x1c9691380d0>],
 'medians': [<matplotlib.lines.Line2D at 0x1c969138f10>],
 'fliers': [<matplotlib.lines.Line2D at 0x1c969145220>],
 'means': []}


tf=pd.read_csv('titanic')
tf.head()


plt.boxplot(tf.Fare)

{'whiskers': [<matplotlib.lines.Line2D at 0x1c975ddb5e0>,
  <matplotlib.lines.Line2D at 0x1c975ddb8b0>],
 'caps': [<matplotlib.lines.Line2D at 0x1c975ddbb80>,
  <matplotlib.lines.Line2D at 0x1c975ddbe50>],
 'boxes': [<matplotlib.lines.Line2D at 0x1c975ddb310>],
 'medians': [<matplotlib.lines.Line2D at 0x1c975deb160>],
 'fliers': [<matplotlib.lines.Line2D at 0x1c975deb430>],
 'means': []}


hf=pd.read_csv('House_price_data')
hf.head()


house.columns

Index(['LotArea', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'FullBath',
       'HalfBath', 'BedroomAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageArea',
       'AboveMedianPrice'],
      dtype='object')


plt.boxplot(hf.LotArea)

{'whiskers': [<matplotlib.lines.Line2D at 0x1c975e3d9d0>,
  <matplotlib.lines.Line2D at 0x1c975e3dca0>],
 'caps': [<matplotlib.lines.Line2D at 0x1c975e3df70>,
  <matplotlib.lines.Line2D at 0x1c975e4d2b0>],
 'boxes': [<matplotlib.lines.Line2D at 0x1c975e3d700>],
 'medians': [<matplotlib.lines.Line2D at 0x1c975e4d580>],
 'fliers': [<matplotlib.lines.Line2D at 0x1c975e4d850>],
 'means': []}


hf['LotArea'].quantile([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1])

0.0      1300.0
0.1      5000.0
0.2      7078.4
0.3      8063.7
0.4      8793.4
0.5      9478.5
0.6     10198.2
0.7     11066.5
0.8     12205.8
0.9     14381.7
1.0    215245.0
Name: LotArea, dtype: float64


plt.boxplot(hf.TotalBsmtSF)

{'whiskers': [<matplotlib.lines.Line2D at 0x1c975ea06a0>,
  <matplotlib.lines.Line2D at 0x1c975ea0970>],
 'caps': [<matplotlib.lines.Line2D at 0x1c975ea0c40>,
  <matplotlib.lines.Line2D at 0x1c975ea0f10>],
 'boxes': [<matplotlib.lines.Line2D at 0x1c975ea03d0>],
 'medians': [<matplotlib.lines.Line2D at 0x1c975ead220>],
 'fliers': [<matplotlib.lines.Line2D at 0x1c975ead4f0>],
 'means': []}


import seaborn as sns
sns.boxplot(hf.TotalBsmtSF)

<AxesSubplot:xlabel='TotalBsmtSF'>


hf['TotalBsmtSF'].quantile([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1])

0.0       0.0
0.1     636.9
0.2     755.8
0.3     840.0
0.4     910.0
0.5     991.5
0.6    1088.0
0.7    1216.0
0.8    1391.2
0.9    1602.2
1.0    6110.0
Name: TotalBsmtSF, dtype: float64


plt.boxplot(hf.GarageArea)

{'whiskers': [<matplotlib.lines.Line2D at 0x1c9761cc700>,
  <matplotlib.lines.Line2D at 0x1c9761cc9d0>],
 'caps': [<matplotlib.lines.Line2D at 0x1c9761ccca0>,
  <matplotlib.lines.Line2D at 0x1c9761ccf70>],
 'boxes': [<matplotlib.lines.Line2D at 0x1c9761cc400>],
 'medians': [<matplotlib.lines.Line2D at 0x1c9761d9280>],
 'fliers': [<matplotlib.lines.Line2D at 0x1c9761d9550>],
 'means': []}


sns.boxplot(hf.GarageArea)

<AxesSubplot:xlabel='GarageArea'>


hf['GarageArea'].quantile([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1])

0.0       0.0
0.1     240.0
0.2     295.6
0.3     384.0
0.4     440.0
0.5     480.0
0.6     516.0
0.7     560.0
0.8     620.2
0.9     757.1
1.0    1418.0
Name: GarageArea, dtype: float64


plt.scatter(hf.LotArea,hf.GarageArea)
plt.xlabel('LotArea')
plt.ylabel('GarageArea')
plt.show()


plt.scatter(hf.TotRmsAbvGrd,hf.LotArea)
plt.xlabel('LotArea')
plt.ylabel('GarageArea')
plt.show()


from scipy .stats import zscore
import numpy as np


#any outlier less than 3 were not showing here
z=np.abs(zscore(hf))
z


threshold=3
print(np.where(z>3))

(array([  53,   53,   88,  144,  166,  178,  185,  185,  188,  189,  191,
        218,  224,  241,  249,  250,  291,  298,  304,  309,  313,  330,
        332,  335,  375,  375,  378,  384,  398,  440,  451,  457,  461,
        496,  508,  519,  523,  533,  570,  581,  583,  597,  605,  624,
        628,  634,  635,  635,  642,  661,  664,  676,  691,  703,  706,
        726,  745,  769,  769,  803,  825,  843,  848,  897,  897,  910,
        921,  980,  991, 1031, 1044, 1061, 1123, 1149, 1154, 1163, 1163,
       1173, 1182, 1190, 1213, 1213, 1230, 1230, 1268, 1270, 1283, 1298,
       1298, 1298, 1298, 1298, 1327, 1350, 1350, 1350, 1352, 1373, 1386,
       1396, 1435, 1450, 1457], dtype=int64), array([0, 6, 2, 6, 8, 9, 2, 7, 5, 6, 2, 2, 3, 2, 0, 2, 6, 5, 2, 8, 0, 6,
       3, 0, 1, 2, 2, 0, 2, 3, 0, 0, 2, 3, 2, 2, 3, 1, 6, 9, 2, 5, 8, 5,
       5, 6, 6, 7, 8, 0, 9, 2, 3, 2, 0, 2, 2, 0, 7, 7, 9, 6, 0, 6, 7, 7,
       5, 2, 2, 7, 3, 9, 2, 2, 5, 5, 6, 7, 3, 9, 2, 6, 5, 7, 2, 6, 5, 0,
       3, 7, 8, 9, 2, 5, 6, 7, 2, 3, 7, 0, 2, 5, 2], dtype=int64))


# array 1 is holding the row number and array 2 is holding the column
#all the values are greater than 3
#z[row][column]
z.iloc[53][0]

3.98424384357879


z.iloc[53,6]

3.514951815977729


z.iloc[88,2]

3.2140273717438843


z.iloc[144,6]

3.842510049724758


Q1=hf.quantile(0.25)
Q3=hf.quantile(0.75)

Q1

LotArea             7553.50
OverallQual            5.00
OverallCond            5.00
TotalBsmtSF          795.75
FullBath               1.00
HalfBath               0.00
BedroomAbvGr           2.00
TotRmsAbvGrd           5.00
Fireplaces             0.00
GarageArea           334.50
AboveMedianPrice       0.00
Name: 0.25, dtype: float64

Q3

LotArea             11601.50
OverallQual             7.00
OverallCond             6.00
TotalBsmtSF          1298.25
FullBath                2.00
HalfBath                1.00
BedroomAbvGr            3.00
TotRmsAbvGrd            7.00
Fireplaces              1.00
GarageArea            576.00
AboveMedianPrice        1.00
Name: 0.75, dtype: float64


IQR=Q3-Q1
IQR

LotArea             4048.0
OverallQual            2.0
OverallCond            1.0
TotalBsmtSF          502.5
FullBath               1.0
HalfBath               1.0
BedroomAbvGr           1.0
TotRmsAbvGrd           2.0
Fireplaces             1.0
GarageArea           241.5
AboveMedianPrice       1.0
dtype: float64


hf_new=hf[(z<3).all(axis=1)]
hf_new


hf.shape

(1460, 11)


hf_new.shape

(1372, 11)


import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib as plt
import warnings
warnings.filterwarnings('ignore')


wine=pd.read_csv('winequality-red.csv')
wine.head()


wine.shape

(1599, 12)


wine.dtypes

fixed acidity           float64
volatile acidity        float64
citric acid             float64
residual sugar          float64
chlorides               float64
free sulfur dioxide     float64
total sulfur dioxide    float64
density                 float64
pH                      float64
sulphates               float64
alcohol                 float64
quality                   int64
dtype: object


wine.columns

Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',
       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',
       'pH', 'sulphates', 'alcohol', 'quality'],
      dtype='object')


wine.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1599 entries, 0 to 1598
Data columns (total 12 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   fixed acidity         1599 non-null   float64
 1   volatile acidity      1599 non-null   float64
 2   citric acid           1599 non-null   float64
 3   residual sugar        1599 non-null   float64
 4   chlorides             1599 non-null   float64
 5   free sulfur dioxide   1599 non-null   float64
 6   total sulfur dioxide  1599 non-null   float64
 7   density               1599 non-null   float64
 8   pH                    1599 non-null   float64
 9   sulphates             1599 non-null   float64
 10  alcohol               1599 non-null   float64
 11  quality               1599 non-null   int64  
dtypes: float64(11), int64(1)
memory usage: 150.0 KB


wine.describe()


wine.quality.unique()

array([5, 6, 7, 4, 8, 3], dtype=int64)


wine.quality.value_counts()

5    681
6    638
7    199
4     53
8     18
3     10
Name: quality, dtype: int64


#To check missing values


sns.heatmap(wine.isnull())

<AxesSubplot:>


dfcor=wine.corr()
dfcor


sns.heatmap(dfcor)

<AxesSubplot:>


plt.figure(figsize=(6,4))
sns.heatmap(dfcor,cmap='Blues',annot=True)

<AxesSubplot:>


plt.figure(figsize=(10,6))
sns.heatmap(dfcor,cmap='Blues',annot=True)

<AxesSubplot:>


wine.columns

Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',
       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',
       'pH', 'sulphates', 'alcohol', 'quality'],
      dtype='object')


#univariable analysis
wine['fixed acidity'].plot.box()

<AxesSubplot:>


wine['density'].plot.box()

<AxesSubplot:>


wine['alcohol'].plot.box()

<AxesSubplot:>


wine['free sulfur dioxide'].plot.box()

<AxesSubplot:>


wine['total sulfur dioxide'].plot.box()

<AxesSubplot:>


wine.shape

(1599, 12)


wine.plot(kind='box',subplots=True,layout=(2,6),figsize=(10,10))

fixed acidity              AxesSubplot(0.125,0.53;0.110714x0.35)
volatile acidity        AxesSubplot(0.257857,0.53;0.110714x0.35)
citric acid             AxesSubplot(0.390714,0.53;0.110714x0.35)
residual sugar          AxesSubplot(0.523571,0.53;0.110714x0.35)
chlorides               AxesSubplot(0.656429,0.53;0.110714x0.35)
free sulfur dioxide     AxesSubplot(0.789286,0.53;0.110714x0.35)
total sulfur dioxide       AxesSubplot(0.125,0.11;0.110714x0.35)
density                 AxesSubplot(0.257857,0.11;0.110714x0.35)
pH                      AxesSubplot(0.390714,0.11;0.110714x0.35)
sulphates               AxesSubplot(0.523571,0.11;0.110714x0.35)
alcohol                 AxesSubplot(0.656429,0.11;0.110714x0.35)
quality                 AxesSubplot(0.789286,0.11;0.110714x0.35)
dtype: object


sns.distplot(wine['density'])

<AxesSubplot:xlabel='density', ylabel='Density'>


sns.distplot(wine['citric acid'])

<AxesSubplot:xlabel='citric acid', ylabel='Density'>


wine.plot(kind='kde',subplots=True,layout=(2,6),figsize=(15,6))

array([[<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,
        <AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,
        <AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>],
       [<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,
        <AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,
        <AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>]],
      dtype=object)


#bivariate analysis
plt.scatter(wine['pH'],wine['quality'])

<matplotlib.collections.PathCollection at 0x1c2459fcd30>


sns.pairplot(wine)

<seaborn.axisgrid.PairGrid at 0x1c24e137670>


# remove the missing values
# drop the negatively correlated columns
#remove the outliers


# Data cleaning is done


plt.scatter(wine['volatile acidity'],wine['quality'])
plt.show()


wine.drop('volatile acidity',axis=1,inplace=True)
wine.head()


wine.shape

(1599, 11)


from scipy.stats import zscore
z=np.abs(zscore(wine))
z


threshold=3
print(np.where(z>3))

(array([  13,   14,   15,   15,   17,   17,   19,   33,   42,   43,   45,
         57,   81,   81,   83,   86,   88,   91,   92,   95,  106,  106,
        109,  142,  144,  147,  151,  151,  151,  151,  163,  164,  169,
        169,  181,  226,  226,  240,  243,  244,  258,  258,  274,  281,
        291,  324,  325,  339,  340,  347,  354,  374,  381,  391,  396,
        396,  400,  400,  442,  442,  451,  459,  467,  480,  480,  494,
        515,  517,  544,  554,  554,  555,  555,  557,  557,  568,  584,
        588,  591,  595,  608,  614,  636,  639,  649,  649,  651,  652,
        652,  652,  672,  684,  690,  692,  692,  695,  723,  730,  754,
        776,  777,  795,  821,  832,  836,  837,  889,  899,  911,  917,
        923,  925,  926,  982, 1017, 1018, 1043, 1051, 1051, 1071, 1074,
       1079, 1079, 1081, 1081, 1111, 1114, 1131, 1154, 1165, 1175, 1186,
       1231, 1235, 1244, 1244, 1244, 1260, 1269, 1269, 1270, 1270, 1288,
       1289, 1295, 1296, 1299, 1300, 1316, 1319, 1319, 1321, 1358, 1367,
       1370, 1370, 1372, 1372, 1374, 1374, 1434, 1434, 1434, 1435, 1435,
       1435, 1469, 1474, 1474, 1474, 1476, 1476, 1476, 1478, 1493, 1496,
       1505, 1558, 1558, 1570, 1574, 1589], dtype=int64), array([ 8,  4,  4,  5,  3,  8,  3,  2,  3,  8,  7,  4,  3,  8,  3,  8,  8,
        8,  8,  7,  3,  8,  5,  9,  9,  3,  1,  3,  7,  8,  2,  2,  3,  8,
        3,  3,  8,  3,  0,  0,  3,  8,  2,  3,  3,  2,  2,  8,  8,  0,  5,
        0,  0,  0,  2,  4,  2,  4,  0,  6,  3, 10,  9,  2,  6,  2,  5, 10,
        0,  0,  6,  0,  6,  0,  6,  3,  4,  9,  5,  2,  6,  8,  5,  8,  2,
        5,  5,  0,  2,  9,  5,  5, 10,  3,  8,  7,  8,  3,  3,  3,  3,  8,
        9, 10,  6,  6,  6, 10,  2,  2,  2,  4,  4,  4,  6,  6,  2,  3,  8,
        2,  2,  2,  5,  2,  5,  7,  6,  4,  4,  3,  4,  2,  4,  2,  2,  4,
        5,  3,  6,  9,  6,  9,  8,  8,  4,  4, 10,  7,  7,  3,  8,  7,  4,
        8,  3,  8,  3,  8,  3, 10,  2,  4,  6,  2,  4,  6, 10,  2,  4,  6,
        2,  4,  6, 10,  5,  5, 10,  3,  4,  3,  2,  2], dtype=int64))


#row and columns
z.iloc[13,8]

5.32210116796055


z.iloc[15,5]

3.087489568732511


wine_new=wine[(z<3).all(axis=1)]
wine_new


wine.shape

(1599, 11)


wine_new.shape

(1458, 11)


import pandas as pd
import seaborn as sns
import warnings 
warnings.filterwarnings('ignore')


df=pd.read_csv('brain_size.csv',sep=';',na_values=".")
df


df.head()


df.head(10)


df.tail()


df.sample()


df.shape

(40, 8)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 40 entries, 0 to 39
Data columns (total 8 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   id         40 non-null     int64  
 1   Gender     40 non-null     object 
 2   FSIQ       40 non-null     int64  
 3   VIQ        40 non-null     int64  
 4   PIQ        40 non-null     int64  
 5   Weight     38 non-null     float64
 6   Height     39 non-null     float64
 7   MRI_Count  40 non-null     int64  
dtypes: float64(2), int64(5), object(1)
memory usage: 2.6+ KB


df.dtypes

id             int64
Gender        object
FSIQ           int64
VIQ            int64
PIQ            int64
Weight       float64
Height       float64
MRI_Count      int64
dtype: object


df.isnull()


df.isnull().sum()

id           0
Gender       0
FSIQ         0
VIQ          0
PIQ          0
Weight       2
Height       1
MRI_Count    0
dtype: int64


sns.heatmap(df.isnull())

<AxesSubplot:>


df.columns

Index(['id', 'Gender', 'FSIQ', 'VIQ', 'PIQ', 'Weight', 'Height', 'MRI_Count'], dtype='object')


df.describe()


sns.countplot(x='Gender',data=df)

<AxesSubplot:xlabel='Gender', ylabel='count'>


sns.displot(df['Height'],bins=10)

<seaborn.axisgrid.FacetGrid at 0x1c2459780a0>


sns.displot(df['Weight'],kde=False,bins=20)

<seaborn.axisgrid.FacetGrid at 0x1c25b85a070>


df['Weight'].plot.box()

<AxesSubplot:>


df.columns

Index(['id', 'Gender', 'FSIQ', 'VIQ', 'PIQ', 'Weight', 'Height', 'MRI_Count'], dtype='object')


sns.boxplot(y='FSIQ',data=df)

<AxesSubplot:ylabel='FSIQ'>


sns.boxplot(y='VIQ',data=df)

<AxesSubplot:ylabel='VIQ'>


df['PIQ'].plot.box()

<AxesSubplot:>


df.isnull().sum()

id           0
Gender       0
FSIQ         0
VIQ          0
PIQ          0
Weight       2
Height       1
MRI_Count    0
dtype: int64


#df.fillna?
import numpy as np
df['Weight'].replace(np.NaN,df['Weight'].mean,inplace=True)


df.isnull().sum()

id           0
Gender       0
FSIQ         0
VIQ          0
PIQ          0
Weight       0
Height       1
MRI_Count    0
dtype: int64


#drop missing values along with rows
df.dropna(inplace=True)


df.isnull().sum()

id           0
Gender       0
FSIQ         0
VIQ          0
PIQ          0
Weight       0
Height       0
MRI_Count    0
dtype: int64


sns.heatmap(df.isnull())

<AxesSubplot:>


sns.heatmap(df.corr())

<AxesSubplot:>


sns.heatmap(df.corr(),annot=True)

<AxesSubplot:>

	market	month	ns_target	gm_target	np_target
0	Australia	2021-09-01	1.165663e+07	5.142392e+06	-1.148236e+06
1	Austria	2021-09-01	1.213599e+06	3.198106e+05	-1.663868e+05
2	Bangladesh	2021-09-01	2.055217e+06	7.955627e+05	5.503872e+03
3	Brazil	2021-09-01	2.282879e+05	6.721141e+04	-2.129470e+04
4	Canada	2021-09-01	1.764242e+07	8.592894e+06	-3.818243e+06
...	...	...	...	...	...
316	South Korea	2022-08-01	2.711193e+07	9.367438e+06	-9.161665e+05
317	Spain	2022-08-01	6.350836e+06	2.657180e+06	5.257603e+05
318	Sweden	2022-08-01	8.366638e+05	3.453694e+05	2.593323e+04
319	United Kingdom	2022-08-01	1.303080e+07	4.817224e+06	-4.839716e+06
320	USA	2022-08-01	6.337197e+07	2.529686e+07	-7.396649e+06

	SNo	Age	Salary
count	22.000000	19.000000	19.000000
mean	12.090909	32.421053	33739.947368
std	6.927578	7.174924	23151.188084
min	1.000000	22.000000	3900.000000
25%	6.250000	27.000000	8888.000000
50%	12.500000	31.000000	33451.000000
75%	17.750000	36.000000	54666.000000
max	23.000000	50.000000	67666.000000

Welcome To Python¶

Data Structures¶

Module¶

File INPUT/OUTPUT OPERATIONS¶

Regular-Expression¶

Numpy¶

PANDAS¶

Reading TSV Format File¶

Reading Excel File¶

Reading HTML File¶

Read Web API File¶

csv¶

Pandas Date Time Functions¶

PANDAS SQL¶

Data Visualization Using Matplotlib¶

Seaborn¶

STATISTICS¶

Hypothesis testing in Machine learning using Python¶

Discover Outliers with mathematical function¶

IQR¶

EXPLORATORY DATA ANALYSIS (EDA)¶

summary statistics¶

Data Visualizations¶

To check Correlation¶

Plotting Outliers¶

To check distribution of skewness¶

Removing Outliers¶

	studentname	studentage
studentid
1	Smith	10
2	John	12
3	jack	10
4	Andrew	17
5	Abdullahi	25
6	Faisal	19

	Gender	AgeRange	HeadSize	BrainWeight
0	1	1	4512	1530
1	1	1	3738	1297
2	1	1	4261	1335
3	1	1	3777	1282
4	1	1	4177	1590
...	...	...	...	...
232	2	2	3214	1110
233	2	2	3394	1215
234	2	2	3233	1104
235	2	2	3352	1170
236	2	2	3391	1120

	SNo	Name	Age	City	Country	Salary
0	1	Tom	28.0	Toronto	Canada	20000.0
1	2	Lee	31.0	Edmonto	Canada	3900.0
2	3	Dave	34.0	Toronto	Canada	8000.0
3	4	Anik	26.0	HongKong	Asia	66672.0
4	5	kamal	30.0	NaN	America	NaN
5	6	Geet	NaN	HongKong	Asia	30007.0
6	7	Steven	43.0	BayArea	America	8300.0
7	8	Ram	38.0	Hyderabad	Asia	54666.0
8	9	hari	50.0	newyork	America	67666.0
9	11	yami	NaN	newyork	America	8888.0
10	12	Anik	26.0	HongKong	Asia	66672.0
11	13	kamal	30.0	NaN	America	34344.0
12	14	Geet	22.0	HongKong	Asia	30007.0
13	15	Steven	43.0	NaN	America	8300.0
14	16	Ram	38.0	Hyderabad	Asia	54666.0
15	17	hari	NaN	newyork	America	67666.0
16	18	yami	27.0	newyork	America	8888.0
17	19	Lee	31.0	Edmonto	Canada	NaN
18	20	Andrew	34.0	Congo	Africa	34432.0
19	21	soham	32.0	Angola	Africa	34534.0
20	22	Grig	26.0	Finland	Europe	33451.0
21	23	Kiara	27.0	London	Europe	NaN

	Name	HireDate	Salary	SickDaysremaining
0	Graham Chapman	03/15/14	50000.0	10
1	John Cleese	06/01/15	65000.0	8
2	Eric Idle	05/12/14	45000.0	10
3	Terry Jones	11/01/13	70000.0	3
4	Terry Gilliam	08/12/14	48000.0	7
5	Michael Palin	05/23/13	66000.0	8

	Name	HireDate	Salary	SickDaysremaining
0	Graham Chapman	2014-03-15	50000.0	10
1	John Cleese	2015-06-01	65000.0	8
2	Eric Idle	2014-05-12	45000.0	10
3	Terry Jones	2013-11-01	70000.0	3
4	Terry Gilliam	2014-08-12	48000.0	7
5	Michael Palin	2013-05-23	66000.0	8

	subject_id	first_name	last_name
0	1	Alex	Ali
1	2	Amy	Abdullahi
2	3	Allen	Faisal
3	4	Abdul	Abdulaziz
4	5	Halima	Basit

	subject_id	first_name	last_name
0	4	Billy	Alimi
1	5	Segun	Basit
2	6	Ali	Fawaz
3	7	Jamal	Kamal
4	8	Sodiq	Abdulyezid

	sepal length	sepal width	petal length	petal width	class
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	Iris-virginica
146	6.3	2.5	5.0	1.9	Iris-virginica
147	6.5	3.0	5.2	2.0	Iris-virginica
148	6.2	3.4	5.4	2.3	Iris-virginica
149	5.9	3.0	5.1	1.8	Iris-virginica

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.0	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

	0	1
0	Alex	10
1	Bob	12
2	clarke	13

	id	Name
0	1	Abdullah
1	2	Muhammad
2	3	Faisal

	id	Name
1	1	Abdullah
2	2	Muhammad
3	3	Faisal

	a	b	c
0	1	2	NaN
1	5	10	20.0

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
0	18.0	8	307.0	130.0	3504	12.0	70	usa	chevrolet chevelle malibu
1	15.0	8	350.0	165.0	3693	11.5	70	usa	buick skylark 320
2	18.0	8	318.0	150.0	3436	11.0	70	usa	plymouth satellite
3	16.0	8	304.0	150.0	3433	12.0	70	usa	amc rebel sst
4	17.0	8	302.0	140.0	3449	10.5	70	usa	ford torino
...	...	...	...	...	...	...	...	...	...
393	27.0	4	140.0	86.0	2790	15.6	82	usa	ford mustang gl
394	44.0	4	97.0	52.0	2130	24.6	82	europe	vw pickup
395	32.0	4	135.0	84.0	2295	11.6	82	usa	dodge rampage
396	28.0	4	120.0	79.0	2625	18.6	82	usa	ford ranger
397	31.0	4	119.0	82.0	2720	19.4	82	usa	chevy s-10

	pickup	dropoff	passengers	distance	fare	tip	tolls	total	color	payment	pickup_zone	dropoff_zone	pickup_borough	dropoff_borough
0	2019-03-23 20:21:09	2019-03-23 20:27:24	1	1.60	7.0	2.15	0.0	12.95	yellow	credit card	Lenox Hill West	UN/Turtle Bay South	Manhattan	Manhattan
1	2019-03-04 16:11:55	2019-03-04 16:19:00	1	0.79	5.0	0.00	0.0	9.30	yellow	cash	Upper West Side South	Upper West Side South	Manhattan	Manhattan
2	2019-03-27 17:53:01	2019-03-27 18:00:25	1	1.37	7.5	2.36	0.0	14.16	yellow	credit card	Alphabet City	West Village	Manhattan	Manhattan
3	2019-03-10 01:23:59	2019-03-10 01:49:51	1	7.70	27.0	6.15	0.0	36.95	yellow	credit card	Hudson Sq	Yorkville West	Manhattan	Manhattan
4	2019-03-30 13:27:42	2019-03-30 13:37:14	3	2.16	9.0	1.10	0.0	13.40	yellow	credit card	Midtown East	Yorkville West	Manhattan	Manhattan
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6428	2019-03-31 09:51:53	2019-03-31 09:55:27	1	0.75	4.5	1.06	0.0	6.36	green	credit card	East Harlem North	Central Harlem North	Manhattan	Manhattan
6429	2019-03-31 17:38:00	2019-03-31 18:34:23	1	18.74	58.0	0.00	0.0	58.80	green	credit card	Jamaica	East Concourse/Concourse Village	Queens	Bronx
6430	2019-03-23 22:55:18	2019-03-23 23:14:25	1	4.14	16.0	0.00	0.0	17.30	green	cash	Crown Heights North	Bushwick North	Brooklyn	Brooklyn
6431	2019-03-04 10:09:25	2019-03-04 10:14:29	1	1.12	6.0	0.00	0.0	6.80	green	credit card	East New York	East Flatbush/Remsen Village	Brooklyn	Brooklyn
6432	2019-03-13 19:31:22	2019-03-13 19:48:02	1	3.85	15.0	3.36	0.0	20.16	green	credit card	Boerum Hill	Windsor Terrace	Brooklyn	Brooklyn

	year	month	passengers
0	1949	Jan	112
1	1949	Feb	118
2	1949	Mar	132
3	1949	Apr	129
4	1949	May	121
...	...	...	...
139	1960	Aug	606
140	1960	Sep	508
141	1960	Oct	461
142	1960	Nov	390
143	1960	Dec	432

	Unnamed: 0	Gender	DOB	Maths	Physics	Chemistry	English	Biology	Economics	History	Civics
0	John	M	05-04-1988	55	45	56	87	21	52	89	65
1	Suresh	M	04-05-1987	75	96	78	64	90	61	58	2
2	Ramesh	M	25-05-1989	25	54	89	76	95	87	56	74
3	Jessica	F	12-08-1990	78	96	86	63	54	89	75	45
4	Jennifer	F	02-09-1989	58	96	78	46	96	77	83	53
5	Annu	F	05-04-1988	45	87	52	89	55	89	87	52
6	pooja	F	04-05-1987	55	64	61	58	75	58	64	61
7	Ritesh	M	25-05-1989	54	76	87	56	25	56	76	87
8	Farha	F	12-08-1990	55	63	89	75	78	75	63	89
9	Mukesh	M	02-09-1989	96	46	77	83	58	83	46	77

	Maths	Physics	Chemistry	English	Biology	Economics	History	Civics
count	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.0000
mean	59.600000	72.300000	75.300000	69.700000	64.700000	72.700000	69.700000	60.5000
std	19.460501	20.661559	14.000397	14.453373	26.998148	14.629119	14.453373	25.3432
min	25.000000	45.000000	52.000000	46.000000	21.000000	52.000000	46.000000	2.0000
25%	54.250000	56.250000	65.000000	59.250000	54.250000	58.750000	59.250000	52.2500
50%	55.000000	70.000000	78.000000	69.500000	66.500000	76.000000	69.500000	63.0000
75%	70.750000	93.750000	86.750000	81.250000	87.000000	86.000000	81.250000	76.2500
max	96.000000	96.000000	89.000000	89.000000	96.000000	89.000000	89.000000	89.0000

	id	Gender	FSIQ	VIQ	PIQ	Weight	Height	MRI_Count
0	1	Female	133	132	124	118.0	64.5	816932
1	2	Male	140	150	124	NaN	72.5	1001121
2	3	Male	139	123	150	143.0	73.3	1038437
3	4	Male	133	129	128	172.0	68.8	965353
4	5	Female	137	132	134	147.0	65.0	951545
5	6	Female	99	90	110	146.0	69.0	928799
6	7	Female	138	136	131	138.0	64.5	991305
7	8	Female	92	90	98	175.0	66.0	854258
8	9	Male	89	93	84	134.0	66.3	904858
9	10	Male	133	114	147	172.0	68.8	955466
10	11	Female	132	129	124	118.0	64.5	833868
11	12	Male	141	150	128	151.0	70.0	1079549
12	13	Male	135	129	124	155.0	69.0	924059
13	14	Female	140	120	147	155.0	70.5	856472
14	15	Female	96	100	90	146.0	66.0	878897
15	16	Female	83	71	96	135.0	68.0	865363
16	17	Female	132	132	120	127.0	68.5	852244
17	18	Male	100	96	102	178.0	73.5	945088
18	19	Female	101	112	84	136.0	66.3	808020
19	20	Male	80	77	86	180.0	70.0	889083
20	21	Male	83	83	86	NaN	NaN	892420
21	22	Male	97	107	84	186.0	76.5	905940
22	23	Female	135	129	134	122.0	62.0	790619
23	24	Male	139	145	128	132.0	68.0	955003
24	25	Female	91	86	102	114.0	63.0	831772
25	26	Male	141	145	131	171.0	72.0	935494
26	27	Female	85	90	84	140.0	68.0	798612
27	28	Male	103	96	110	187.0	77.0	1062462
28	29	Female	77	83	72	106.0	63.0	793549
29	30	Female	130	126	124	159.0	66.5	866662
30	31	Female	133	126	132	127.0	62.5	857782
31	32	Male	144	145	137	191.0	67.0	949589
32	33	Male	103	96	110	192.0	75.5	997925
33	34	Male	90	96	86	181.0	69.0	879987
34	35	Female	83	90	81	143.0	66.5	834344
35	36	Female	133	129	128	153.0	66.5	948066
36	37	Male	140	150	124	144.0	70.5	949395
37	38	Female	88	86	94	139.0	64.5	893983
38	39	Male	81	90	74	148.0	74.0	930016
39	40	Male	89	91	89	179.0	75.5	935863

	Maths	Physics	Chemistry	English	Biology	Economics	History
0	55	45	56	87	21	52	89
1	75	96	78	64	90	61	58
2	25	54	89	76	95	87	56
3	78	96	86	63	54	89	75
4	58	96	78	46	96	77	83
5	45	87	52	89	55	89	87
6	55	64	61	58	75	58	64
7	54	76	87	56	25	56	76
8	55	63	89	75	78	75	63
9	96	46	77	83	58	83	46

	Maths	Physics	Chemistry	English	Biology	Economics	History	Civics
Maths	1.000000	0.113354	0.076751	-0.064074	-0.146598	0.000312	-0.320846	-0.274629
Physics	0.113354	1.000000	0.117192	-0.562608	0.279638	0.191851	0.334082	-0.643266
Chemistry	0.076751	0.117192	1.000000	-0.409680	0.318032	0.235390	-0.482710	0.210908
English	-0.064074	-0.562608	-0.409680	1.000000	-0.330844	0.237577	-0.038775	0.186705
Biology	-0.146598	0.279638	0.318032	-0.330844	1.000000	0.370531	-0.456131	-0.330060
Economics	0.000312	0.191851	0.235390	0.237577	0.370531	1.000000	-0.164953	0.043905
History	-0.320846	0.334082	-0.482710	-0.038775	-0.456131	-0.164953	1.000000	-0.048686
Civics	-0.274629	-0.643266	0.210908	0.186705	-0.330060	0.043905	-0.048686	1.000000

	order	name	height
0	1	George Washington	189
1	2	John Adams	170
2	3	Thomas Jefferson	189
3	4	James Madison	163
4	5	James Monroe	183
5	6	John Quincy Adams	171
6	7	Andrew Jackson	185
7	8	Martin Van Buren	168
8	9	William Henry Harrison	173
9	10	John Tyler	183
10	11	James K. Polk	173
11	12	Zachary Taylor	173
12	13	Millard Fillmore	175
13	14	Franklin Pierce	178
14	15	James Buchanan	183
15	16	Abraham Lincoln	193
16	17	Andrew Johnson	178
17	18	Ulysses S. Grant	173
18	19	Rutherford B. Hayes	174
19	20	James A. Garfield	183
20	21	Chester A. Arthur	183
21	23	Benjamin Harrison	168
22	25	William McKinley	170
23	26	Theodore Roosevelt	178
24	27	William Howard Taft	182
25	28	Woodrow Wilson	180
26	29	Warren G. Harding	183
27	30	Calvin Coolidge	178
28	31	Herbert Hoover	182
29	32	Franklin D. Roosevelt	188
30	33	Harry S. Truman	175
31	34	Dwight D. Eisenhower	179
32	35	John F. Kennedy	183
33	36	Lyndon B. Johnson	193
34	37	Richard Nixon	182
35	38	Gerald Ford	183
36	39	Jimmy Carter	177
37	40	Ronald Reagan	185
38	41	George H. W. Bush	188
39	42	Bill Clinton	188
40	43	George W. Bush	182
41	44	Barack Obama	185

	satisfaction_level	last_evaluation	number_project	average_montly_hours	time_spend_company	left	Departments	salary
0	0.38	0.53	2	157	3	1	sales	low
1	0.80	0.86	5	262	6	1	sales	medium
2	0.11	0.88	7	272	4	1	sales	medium
3	0.72	0.87	5	223	5	1	sales	low
4	0.37	0.52	2	159	3	1	sales	low

	Maths	Physics	Chemistry	English	Biology	Economics	History
0	55	45	56	87	21	52	89
1	75	96	78	64	90	61	58
2	25	54	89	76	95	87	56
3	78	96	86	63	54	89	75
4	58	96	78	46	96	77	83
5	45	87	52	89	55	89	87
6	55	64	61	58	75	58	64
7	54	76	87	56	25	56	76
8	55	63	89	75	78	75	63
9	96	46	77	83	58	83	46

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

	LotArea	OverallQual	OverallCond	TotalBsmtSF	FullBath	HalfBath	BedroomAbvGr	TotRmsAbvGrd	Fireplaces	GarageArea	AboveMedianPrice
0	8450	7	5	856	2	1	3	8	0	548	1
1	9600	6	8	1262	2	0	3	6	1	460	1
2	11250	7	5	920	2	1	3	6	1	608	1
3	9550	7	5	756	1	0	3	7	1	642	0
4	14260	8	5	1145	2	1	4	9	1	836	1

	LotArea	OverallQual	OverallCond	TotalBsmtSF	FullBath	HalfBath	BedroomAbvGr	TotRmsAbvGrd	Fireplaces	GarageArea	AboveMedianPrice
0	0.207142	0.651479	0.517200	0.459303	0.789741	1.227585	0.163779	0.912210	0.951226	0.351000	1.002743
1	0.091886	0.071836	2.179628	0.466465	0.789741	0.761621	0.163779	0.318683	0.600495	0.060731	1.002743
2	0.073480	0.651479	0.517200	0.313369	0.789741	1.227585	0.163779	0.318683	0.600495	0.631726	1.002743
3	0.096897	0.651479	0.517200	0.687324	1.026041	0.761621	0.163779	0.296763	0.600495	0.790804	0.997264
4	0.375148	1.374795	0.517200	0.199680	0.789741	1.227585	1.390023	1.527656	0.600495	1.698485	1.002743
...	...	...	...	...	...	...	...	...	...	...	...
1455	0.260560	0.071836	0.517200	0.238122	0.789741	1.227585	0.163779	0.296763	0.600495	0.060731	1.002743
1456	0.266407	0.071836	0.381743	1.104925	0.789741	0.761621	0.163779	0.296763	2.152216	0.126420	1.002743
1457	0.147810	0.651479	3.078570	0.215641	0.789741	0.761621	1.390023	1.527656	2.152216	1.033914	1.002743
1458	0.080160	0.795151	0.381743	0.046905	1.026041	0.761621	1.062465	0.934130	0.951226	1.090059	0.997264
1459	0.058112	0.795151	0.381743	0.452784	1.026041	1.227585	0.163779	0.318683	0.951226	0.921624	0.997264

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5
1	7.8	0.88	0.00	2.6	0.098	25.0	67.0	0.9968	3.20	0.68	9.8	5
2	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.9970	3.26	0.65	9.8	5
3	11.2	0.28	0.56	1.9	0.075	17.0	60.0	0.9980	3.16	0.58	9.8	6
4	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
count	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000
mean	8.319637	0.527821	0.270976	2.538806	0.087467	15.874922	46.467792	0.996747	3.311113	0.658149	10.422983	5.636023
std	1.741096	0.179060	0.194801	1.409928	0.047065	10.460157	32.895324	0.001887	0.154386	0.169507	1.065668	0.807569
min	4.600000	0.120000	0.000000	0.900000	0.012000	1.000000	6.000000	0.990070	2.740000	0.330000	8.400000	3.000000
25%	7.100000	0.390000	0.090000	1.900000	0.070000	7.000000	22.000000	0.995600	3.210000	0.550000	9.500000	5.000000
50%	7.900000	0.520000	0.260000	2.200000	0.079000	14.000000	38.000000	0.996750	3.310000	0.620000	10.200000	6.000000
75%	9.200000	0.640000	0.420000	2.600000	0.090000	21.000000	62.000000	0.997835	3.400000	0.730000	11.100000	6.000000
max	15.900000	1.580000	1.000000	15.500000	0.611000	72.000000	289.000000	1.003690	4.010000	2.000000	14.900000	8.000000

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
fixed acidity	1.000000	-0.256131	0.671703	0.114777	0.093705	-0.153794	-0.113181	0.668047	-0.682978	0.183006	-0.061668	0.124052
volatile acidity	-0.256131	1.000000	-0.552496	0.001918	0.061298	-0.010504	0.076470	0.022026	0.234937	-0.260987	-0.202288	-0.390558
citric acid	0.671703	-0.552496	1.000000	0.143577	0.203823	-0.060978	0.035533	0.364947	-0.541904	0.312770	0.109903	0.226373
residual sugar	0.114777	0.001918	0.143577	1.000000	0.055610	0.187049	0.203028	0.355283	-0.085652	0.005527	0.042075	0.013732
chlorides	0.093705	0.061298	0.203823	0.055610	1.000000	0.005562	0.047400	0.200632	-0.265026	0.371260	-0.221141	-0.128907
free sulfur dioxide	-0.153794	-0.010504	-0.060978	0.187049	0.005562	1.000000	0.667666	-0.021946	0.070377	0.051658	-0.069408	-0.050656
total sulfur dioxide	-0.113181	0.076470	0.035533	0.203028	0.047400	0.667666	1.000000	0.071269	-0.066495	0.042947	-0.205654	-0.185100
density	0.668047	0.022026	0.364947	0.355283	0.200632	-0.021946	0.071269	1.000000	-0.341699	0.148506	-0.496180	-0.174919
pH	-0.682978	0.234937	-0.541904	-0.085652	-0.265026	0.070377	-0.066495	-0.341699	1.000000	-0.196648	0.205633	-0.057731
sulphates	0.183006	-0.260987	0.312770	0.005527	0.371260	0.051658	0.042947	0.148506	-0.196648	1.000000	0.093595	0.251397
alcohol	-0.061668	-0.202288	0.109903	0.042075	-0.221141	-0.069408	-0.205654	-0.496180	0.205633	0.093595	1.000000	0.476166
quality	0.124052	-0.390558	0.226373	0.013732	-0.128907	-0.050656	-0.185100	-0.174919	-0.057731	0.251397	0.476166	1.000000

	fixed acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	0.528360	1.391472	0.453218	0.243707	0.466193	0.379133	0.558274	1.288643	0.579207	0.960246	0.787823
1	0.298547	1.391472	0.043416	0.223875	0.872638	0.624363	0.028261	0.719933	0.128950	0.584777	0.787823
2	0.298547	1.186070	0.169427	0.096353	0.083669	0.229047	0.134264	0.331177	0.048089	0.584777	0.787823
3	1.654856	1.484154	0.453218	0.264960	0.107592	0.411500	0.664277	0.979104	0.461180	0.584777	0.450848
4	0.528360	1.391472	0.453218	0.243707	0.466193	0.379133	0.558274	1.288643	0.579207	0.960246	0.787823
...	...	...	...	...	...	...	...	...	...	...	...
1594	1.217796	0.980669	0.382271	0.053845	1.542054	0.075043	0.978765	0.899886	0.461180	0.072294	0.787823
1595	1.390155	0.877968	0.240375	0.541259	2.211469	0.137820	0.862162	1.353436	0.601055	0.729364	0.450848
1596	1.160343	0.723916	0.169427	0.243707	1.255161	0.196679	0.533554	0.705508	0.542042	0.541630	0.450848
1597	1.390155	0.775267	0.382271	0.264960	1.542054	0.075043	0.676657	1.677400	0.305990	0.209308	0.787823
1598	1.332702	1.021999	0.752894	0.434990	0.203223	0.135861	0.666057	0.511130	0.010924	0.541630	0.450848

	id	FSIQ	VIQ	PIQ	Weight	Height	MRI_Count
count	40.000000	40.000000	40.000000	40.00000	38.000000	39.000000	4.000000e+01
mean	20.500000	113.450000	112.350000	111.02500	151.052632	68.525641	9.087550e+05
std	11.690452	24.082071	23.616107	22.47105	23.478509	3.994649	7.228205e+04
min	1.000000	77.000000	71.000000	72.00000	106.000000	62.000000	7.906190e+05
25%	10.750000	89.750000	90.000000	88.25000	135.250000	66.000000	8.559185e+05
50%	20.500000	116.500000	113.000000	115.00000	146.500000	68.000000	9.053990e+05
75%	30.250000	135.500000	129.750000	128.00000	172.000000	70.500000	9.500780e+05
max	40.000000	144.000000	150.000000	150.00000	192.000000	77.000000	1.079549e+06